﻿--------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\ecedwar2\Dropbox\Land and Water\empirics\JAERE_Replication
> \SEL_Winters_Replication\replication_log.log
  log type:  text
 opened on:  28 Mar 2023, 13:59:47

. 
. use "WintersData.dta", clear

. 
. 
. *Table 1: Main Results, Figure 3, and Figure A6
. do "run_table_1.do"

. 
. *This file runs the regressions for Table 1, as well as Figures 3 and A6
. 
. *Table 1, Panel A: Y = % Ag, did_multiplegt estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. *No Controls
. *Note: this command also generates Figure 3
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10)   robust_dynamic longdiff_placebo 
> covariances average_effect graphoptions (ytitle(Agriculture (%)) graphregion(c
> olor(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpattern(
> dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .3003763    .052033   .1983916   .4023611     995583     151816 
    Effect_1 |  .6328919   .0959671   .4447964   .8209875     664347     102411 
    Effect_2 |  1.302262   .1360091   1.035684    1.56884     339795      29948 
     Average |  .5257928   .0654474    .397516   .6540696    1999725     284175 
   Placebo_1 |  .0534376   .0654148  -.0747754   .1816506     713546     151610 
   Placebo_2 |  .1399883   .0686534   .0054275    .274549     202684      72463 

. 
. *off-rez population control
. *Note: this command also generates Panel A of Figure A6
. 
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop ) robust_dynami
> c longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture
>  (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20)
>  yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .3265771   .0499246   .2287249   .4244294     995577     151816 
    Effect_1 |  .6957092   .0961981    .507161   .8842575     664338     102411 
    Effect_2 |  1.541808   .1760181   1.196812   1.886803     339789      29948 
     Average |   .587673   .0653842     .45952    .715826    1999704     284175 
   Placebo_1 |  .0329625   .0638532  -.0921898   .1581149     713537     151610 
   Placebo_2 |  .1349946   .0691468  -.0005332   .2705224     202681      72463 

. 
. *casino control
. *Note: this command also generates Panel B of Figure A6
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino ) robust_dynam
> ic longdiff_placebo covariances average_effect graphoptions (ytitle(Agricultur
> e (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20
> ) yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .3490517   .0517855   .2475521   .4505512     995583     151816 
    Effect_1 |   .652337   .0932527   .4695618   .8351122     664347     102411 
    Effect_2 |   1.52185   .1248306   1.277182   1.766519     339795      29948 
     Average |   .581946   .0611153     .46216    .701732    1999725     284175 
   Placebo_1 | -.0039626   .0619542  -.1253929   .1174677     713546     151610 
   Placebo_2 |  .0689868   .0630314  -.0545547   .1925284     202684      72463 

. 
. *credit control
. *Note: this command also generates Panel C of Figure A6
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit ) robust_dynam
> ic longdiff_placebo covariances average_effect graphoptions (ytitle(Agricultur
> e (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20
> ) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .3047414   .0595617   .1880003   .4214824     995583     151816 
    Effect_1 |   .631063   .0937328   .4473467   .8147793     664347     102411 
    Effect_2 |   1.29104   .1544657   .9882871   1.593793     339795      29948 
     Average |   .526283   .0661918   .3965471   .6560189    1999725     284175 
   Placebo_1 |  .0543973   .0644903  -.0720037   .1807984     713546     151610 
   Placebo_2 |   .132876   .0723903  -.0090089    .274761     202684      72463 

. 
. *all rez-t controls
. *Note: this command also generates Panel D of Figure A6
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_
> credit ) robust_dynamic longdiff_placebo covariances average_effect graphoptio
> ns (ytitle(Agriculture (%)) xtitle(Time to Treatment) graphregion(color(white)
> )  ysize(15) xsize(20) xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .3665469   .0574768   .2538924   .4792014     995577     151816 
    Effect_1 |   .682826   .0970299   .4926474   .8730046     664338     102411 
    Effect_2 |  1.628537    .196294     1.2438   2.013273     339789      29948 
     Average |  .6135233   .0664839   .4832149   .7438317    1999704     284175 
   Placebo_1 | -.0118686   .0604209  -.1302937   .1065564     713537     151610 
   Placebo_2 |  .0601773   .0643611  -.0659704   .1863251     202681      72463 

. 
. 
. *Table 1, Panel B: Y = % Ag, csdid estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. preserve

. 
. 
. eststo clear

. *Baseline with no rezxt controls
. csdid agpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .3918349   .0617864     6.34   0.000     .2707357    .5129341
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid agpct offrespop, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple
> ) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .3417532    .160114     2.13   0.033     .0279356    .6555708
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid agpct has_casino, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .4080812   .0604324     6.75   0.000     .2896359    .5265265
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid agpct has_credit, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .4566145   .1136601     4.02   0.000     .2338449    .6793841
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid agpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  cluste
> r(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .5000078   .2205066     2.27   0.023     .0678228    .9321928
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. restore

. 
. *Table 1, Panel C: Y = % Ag, twfe estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. eststo clear

. *Baseline with no rezxt controls
. reghdfe agpct post , absorb(ID stateXyear START) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 3 HDFE groups                           F(   1,   2630) =       9.47
Statistics robust to heteroskedasticity           Prob > F        =     0.0021
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0002
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5421

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2094143   .0680616     3.08   0.002     .0759547     .342874
       _cons |   8.104314   .0137254   590.46   0.000       8.0774    8.131227
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
    STARTDUM |         7           1           6     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100

. estadd scalar MDV = r(mean)

. est sto twfe_ag_1

. 
. *off-rez population
. reghdfe agpct post offrespop  , absorb(ID stateXyear START) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 3 HDFE groups                           F(   2,   2630) =      11.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0004
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5417

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2920057   .0675461     4.32   0.000     .1595568    .4244546
   offrespop |  -1.83e-07   5.91e-08    -3.10   0.002    -2.99e-07   -6.70e-08
       _cons |   8.164173   .0257384   317.20   0.000     8.113703    8.214643
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
    STARTDUM |         7           1           6     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

. estadd scalar MDV = r(mean)

. est sto twfe_ag_2

. 
. *credit
. reghdfe agpct post has_casino  , absorb(ID stateXyear START) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 3 HDFE groups                           F(   2,   2630) =      59.42
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0010
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3477229   .0645692     5.39   0.000     .2211114    .4743345
  has_casino |  -.4732857   .0526401    -8.99   0.000     -.576506   -.3700654
       _cons |   8.172884   .0175549   465.56   0.000     8.138461    8.207307
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
    STARTDUM |         7           1           6     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100

. estadd scalar MDV = r(mean)

. est sto twfe_ag_3

. 
. * casinos
. reghdfe agpct post has_credit , absorb(ID stateXyear START) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 3 HDFE groups                           F(   2,   2630) =       5.24
Statistics robust to heteroskedasticity           Prob > F        =     0.0054
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5420

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2011411   .0671254     3.00   0.003     .0695171    .3327651
  has_credit |  -.1177999   .0801653    -1.47   0.142    -.2749933    .0393935
       _cons |   8.166984   .0425695   191.85   0.000     8.083511    8.250457
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
    STARTDUM |         7           1           6     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100

. estadd scalar MDV = r(mean)

. est sto twfe_ag_4

. 
. 
. *all rez-t controls
. reghdfe agpct post offrespop has_c* ,  absorb(ID stateXyear START) cluster(TOW
> NSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 3 HDFE groups                           F(   4,   2630) =      31.46
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3603735   .0638532     5.64   0.000     .2351658    .4855811
   offrespop |  -5.42e-08   5.83e-08    -0.93   0.353    -1.68e-07    6.01e-08
  has_casino |  -.4420043   .0519065    -8.52   0.000     -.543786   -.3402227
  has_credit |  -.0382617   .0806138    -0.47   0.635    -.1963345    .1198111
       _cons |   8.206461   .0449644   182.51   0.000     8.118292     8.29463
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
    STARTDUM |         7           1           6     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

. estadd scalar MDV = r(mean)

. est sto twfe_ag_5

. 
. 
. esttab twfe_ag_1 twfe_ag_2 twfe_ag_3 twfe_ag_4 twfe_ag_5 ,  se(a3) b(a3) star(
> * 0.1 ** 0.05 *** 0.01) ar2  replace   scalar(N_clust M1 MDV)

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                    agpct           agpct           agpct           agpct       
>     agpct   
--------------------------------------------------------------------------------
> ------------
post                0.209***        0.292***        0.348***        0.201***    
>     0.360***
                 (0.0681)        (0.0675)        (0.0646)        (0.0671)       
>  (0.0639)   

offrespop                    -0.000000183***                                    
> -5.42e-08   
                               (5.91e-08)                                      (
> 5.83e-08)   

has_casino                                         -0.473***                    
>    -0.442***
                                                 (0.0526)                       
>  (0.0519)   

has_credit                                                         -0.118       
>   -0.0383   
                                                                 (0.0802)       
>  (0.0806)   

_cons               8.104***        8.164***        8.173***        8.167***    
>     8.206***
                 (0.0137)        (0.0257)        (0.0176)        (0.0426)       
>  (0.0450)   
--------------------------------------------------------------------------------
> ------------
N                 1410185         1410182         1410185         1410185       
>   1410182   
adj. R-sq           0.979           0.979           0.979           0.979       
>     0.979   
N_clust              2631            2631            2631            2631       
>      2631   
M1                                                                              
>             
MDV                 8.147           8.147           8.147           8.147       
>     8.147   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. 
. 
end of do-file

. 
. *Table A1: Pre-Settlement Parcel Summary Statistics (1974)
. do "run_table_A1.do"

. *This file produces Table A1
. 
. 
. preserve

. 
. keep if year == 1974 
(1,129,927 observations deleted)

. 
. balancetable Treated agpct devpct SoilMean ElevMean Ruggedness StreamDist Fee 
> Allotted Tribal BIA using "winters_balance.xls", vce(cluster TOWNSHIP) replace

. 
. restore

. 
end of do-file

. 
. *Table A2: Time-Varying Summary Statistics
. do "run_table_A2.do"

. *This file creates the excel files necessary to create Table A2
. 
. egen STATE = group(state_code)
(3 missing values generated)

. 
. 
. 
. *This first chunk produces columns 1--3
. ******************************************************************************
> *
. preserve

. 
. keep if year == 1974 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using  "winters_bal_
> 1974.xls", replace vce(cluster TOWNSHIP)

. 
. restore

. 
. preserve

.  
. keep if year == 1982 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_1
> 982.xls", replace vce(cluster TOWNSHIP)

. restore

. 
. 
. preserve

. 
. keep if year == 1992 & e(sample)==1
(1,130,374 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_1
> 992.xls", replace vce(cluster TOWNSHIP)

. 
. restore

. 
. 
. preserve

. 
. keep if year == 2002 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_2
> 002.xls", replace vce(cluster TOWNSHIP)

. 
. restore

. 
. 
. preserve

. 
. keep if year == 2012 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_2
> 012.xls", replace vce(cluster TOWNSHIP)

. 
. restore

. 
. 
. 
. 
. *This chunk produces the within-state comparisons in column 4
. ******************************************************************************
> *
. 
. preserve

. 
. keep if year == 1974 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using  "winters_bal_
> 1974_state.xls", vce(cluster TOWNSHIP) fe(STATE) replace

. 
. restore

. 
. preserve

. 
. keep if year == 1982 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_1
> 982_state.xls", vce(cluster TOWNSHIP) fe(STATE) replace

. 
. restore

. 
. 
. preserve

. 
. keep if year == 1992 & e(sample)==1
(1,130,374 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_1
> 992_state.xls", vce(cluster TOWNSHIP) fe(STATE) replace 

. 
. restore

. 
. 
. preserve

. 
. keep if year == 2002 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_2
> 002_state.xls", vce(cluster TOWNSHIP) fe(STATE) replace 

. 
. restore

. 
. 
. preserve

. 
. keep if year == 2012 & e(sample)==1
(1,130,371 observations deleted)

. 
. balancetable Treated post offrespop has_casino has_credit using "winters_bal_2
> 012_state.xls", vce(cluster TOWNSHIP) fe(STATE) replace

. 
. restore

. 
end of do-file

. 
. *Table A3: Alternative Outcome Variables
. do "run_table_A3.do"

. *This file runs the regressions to create Table A3
. 
. 
. 
. **Table A3, Panel A: Alternative Outcome Variables, did_multiplegt estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. *No rez-t controls: Y= % crops
. did_multiplegt croppct ID        year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10)   robust_dynamic longdiff_
> placebo covariances average_effect graphoptions (ytitle(Row Crops (%)) graphre
> gion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpa
> ttern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |   .060258   .0456082   -.029134   .1496501     995583     151816 
    Effect_1 |  .3180466   .0659738    .188738   .4473552     664347     102411 
    Effect_2 |  .7168652   .1523269   .4183044   1.015426     339795      29948 
     Average |   .222357   .0589553   .1068045   .3379094    1999725     284175 
   Placebo_1 | -.1150405   .0610321  -.2346635   .0045825     713546     151610 
   Placebo_2 |  .1222469   .0407184   .0424388    .202055     202684      72463 

. 
. 
. *all rez-t controls: Y= % crops
. did_multiplegt croppct ID        year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_cas
> ino has_credit ) robust_dynamic longdiff_placebo covariances average_effect gr
> aphoptions (ytitle(Row Crops (%)) xtitle(Time to Treatment) graphregion(color(
> white))  ysize(15) xsize(20) xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0830117   .0513186  -.0175728   .1835963     995577     151816 
    Effect_1 |  .3240346   .0921067   .1435055   .5045636     664338     102411 
    Effect_2 |  .6141965   .3070807   .0123182   1.216075     339789      29948 
     Average |  .2258509   .0861732   .0569514   .3947504    1999704     284175 
   Placebo_1 | -.0827994   .0589904  -.1984205   .0328218     713537     151610 
   Placebo_2 |  .1004155   .0476034   .0071128   .1937182     202681      72463 

. 
. 
. *No rez-t controls: Y= 1(% ag > 0)
. did_multiplegt AgDum ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10)   robust_dynamic longdiff_placebo 
> covariances average_effect graphoptions (ytitle(Probability of Agriculture) gr
> aphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0
> , lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0110931     .00133   .0084864   .0136999     995583     151816 
    Effect_1 |  .0199631   .0033326   .0134311    .026495     664347     102411 
    Effect_2 |  .0517203    .007078   .0378474   .0655933     339795      29948 
     Average |  .0185712   .0024467   .0137756   .0233668    1999725     284175 
   Placebo_1 |  .0062073    .001228   .0038004   .0086142     713546     151610 
   Placebo_2 |  .0057798   .0012952   .0032412   .0083185     202684      72463 

. 
. 
. *all rez-t controls: Y=  1(% ag > 0)
. did_multiplegt AgDum ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_
> credit ) robust_dynamic longdiff_placebo covariances average_effect graphoptio
> ns (ytitle(Probability of Agriculture) xtitle(Time to Treatment) graphregion(c
> olor(white))  ysize(15) xsize(20) xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0126548    .001528     .00966   .0156497     995577     151816 
    Effect_1 |   .020093   .0033713   .0134852   .0267008     664338     102411 
    Effect_2 |  .0560547   .0066159   .0430875   .0690219     339789      29948 
     Average |  .0199091   .0025347   .0149412   .0248771    1999704     284175 
   Placebo_1 |   .004739   .0013495   .0020939   .0073841     713537     151610 
   Placebo_2 |  .0032936   .0013494   .0006487   .0059385     202681      72463 

. 
. 
. 
. *No rez-t controls: Y= % development
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10)   robust_dynamic longdiff_
> placebo covariances average_effect graphoptions (ytitle(Development (%)) graph
> region(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, l
> pattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0061958   .0540401  -.1121145   .0997228     995583     151816 
    Effect_1 | -.0753954   .1433011  -.3562655   .2054747     664347     102411 
    Effect_2 |   -.11146   .2312614  -.5647323   .3418123     339795      29948 
     Average | -.0422273   .1005425  -.2392907    .154836    1999725     284175 
   Placebo_1 |   .210969   .2131338  -.2067733   .6287114     713546     151610 
   Placebo_2 |  .0517239   .0477477  -.0418616   .1453093     202684      72463 

. 
. 
. *all rez-t controls: Y= % development
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_cas
> ino has_credit ) robust_dynamic longdiff_placebo covariances average_effect gr
> aphoptions (ytitle(Development (%)) xtitle(Time to Treatment) graphregion(colo
> r(white))  ysize(15) xsize(20) xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0132982   .0435394  -.0986354   .0720389     995577     151816 
    Effect_1 | -.1232778    .125289  -.3688442   .1222886     664338     102411 
    Effect_2 | -.2789204   .2733327  -.8146525   .2568117     339789      29948 
     Average | -.0809255   .0854266  -.2483617   .0865107    1999704     284175 
   Placebo_1 |  .2196048   .1934988  -.1596529   .5988624     713537     151610 
   Placebo_2 |  .0354302   .0524111  -.0672956   .1381561     202681      72463 

. 
. 
. 
. **Table A3, Panel B: Alternative Outcome Variables, csdid estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. preserve

. *keep if dropthis ==0  //Drops 206 observations that appear only in 2012 due t
> o GIS processing issues (creates a balanced panel, other estimators do this au
> tomatically)
. 
. eststo clear

. *Baseline with no rezxt controls: Y= % crops
. csdid croppct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .1402809   .0499633     2.81   0.005     .0423546    .2382072
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls: Y= % crops
. csdid croppct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  clus
> ter(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .1181345   .1576126     0.75   0.454    -.1907805    .4270495
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. *Baseline with no rezxt controls: Y=  1(% ag > 0)
. csdid AgDum , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .0100848   .0014146     7.13   0.000     .0073123    .0128573
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls: Y=  1(% ag > 0)
. csdid AgDum offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  cluste
> r(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .0236666   .0041381     5.72   0.000      .015556    .0317771
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. *Baseline with no rezxt controls: Y= % development
. csdid devpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .0954866   .1419142     0.67   0.501    -.1826602    .3736334
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls: Y= % development
. csdid devpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  clust
> er(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.2823913    .232939    -1.21   0.225    -.7389434    .1741608
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. restore

. 
. 
. **Table A3, Panel C: Alternative Outcome Variables, twfe estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. eststo clear

. *Baseline with no rezxt controls: Y= % crops
. reghdfe croppct post , absorb(ID stateXyear ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   1,   2630) =      10.68
Statistics robust to heteroskedasticity           Prob > F        =     0.0011
                                                  R-squared       =     0.9843
                                                  Adj R-squared   =     0.9804
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.1211

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
     croppct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2208561   .0675896     3.27   0.001     .0883218    .3533903
       _cons |   6.427797   .0136303   471.58   0.000      6.40107    6.454524
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_1

. 
. *all rez-t controls: Y= % crops
. reghdfe croppct post offrespop has_c* ,  absorb(ID stateXyear ) cluster(TOWNSH
> IP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   4,   2630) =      11.71
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9843
                                                  Adj R-squared   =     0.9804
                                                  Within R-sq.    =     0.0008
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.1204

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
     croppct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3488078   .0674713     5.17   0.000     .2165056    .4811101
   offrespop |  -1.68e-07   5.15e-08    -3.25   0.001    -2.69e-07   -6.65e-08
  has_casino |  -.1689721   .0548354    -3.08   0.002    -.2764971   -.0614471
  has_credit |   .0410414   .0711688     0.58   0.564     -.098511    .1805939
       _cons |     6.4853   .0332435   195.08   0.000     6.420114    6.550486
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_2

. 
. *Baseline with no rezxt controls: Y=  1(% ag > 0)
. reghdfe AgDum post , absorb(ID stateXyear ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   1,   2630) =      21.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9584
                                                  Adj R-squared   =     0.9480
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     0.0804

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       AgDum | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .0060246   .0013098     4.60   0.000     .0034563    .0085929
       _cons |   .1441539   .0002641   545.76   0.000      .143636    .1446719
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_3

. 
. *all rez-t controls: Y=  1(% ag > 0)
. reghdfe AgDum post offrespop has_c* ,  absorb(ID stateXyear ) cluster(TOWNSHIP
> )
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   4,   2630) =      21.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9584
                                                  Adj R-squared   =     0.9480
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     0.0804

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       AgDum | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .0091455   .0015088     6.06   0.000      .006187     .012104
   offrespop |  -7.76e-12   7.44e-10    -0.01   0.992    -1.47e-09    1.45e-09
  has_casino |  -.0103638   .0012794    -8.10   0.000    -.0128725   -.0078551
  has_credit |   .0012647   .0013504     0.94   0.349    -.0013832    .0039127
       _cons |   .1449854   .0006346   228.47   0.000     .1437411    .1462298
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_4

. 
. *Baseline with no rezxt controls: Y= % development
. reghdfe devpct post , absorb(ID stateXyear ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   1,   2630) =       0.10
Statistics robust to heteroskedasticity           Prob > F        =     0.7488
                                                  R-squared       =     0.9103
                                                  Adj R-squared   =     0.8879
                                                  Within R-sq.    =     0.0000
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.2112

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
      devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.0522022    .162983    -0.32   0.749    -.3717902    .2673857
       _cons |   1.358361   .0328675    41.33   0.000     1.293912     1.42281
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_5

. 
. *all rez-t controls: Y= % development
. reghdfe devpct post offrespop has_c* ,  absorb(ID stateXyear ) cluster(TOWNSHI
> P)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   4,   2630) =       2.29
Statistics robust to heteroskedasticity           Prob > F        =     0.0571
                                                  R-squared       =     0.9104
                                                  Adj R-squared   =     0.8880
                                                  Within R-sq.    =     0.0005
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.2103

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
      devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.0765603   .1170151    -0.65   0.513    -.3060113    .1528908
   offrespop |   1.38e-07   1.13e-07     1.22   0.221    -8.29e-08    3.59e-07
  has_casino |  -.0665451   .2097177    -0.32   0.751    -.4777734    .3446832
  has_credit |   .2627623   .1414199     1.86   0.063    -.0145433    .5400679
       _cons |   1.182988   .0649104    18.22   0.000     1.055707    1.310269
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_6

. 
. esttab twfe_alt_1 twfe_alt_2 twfe_alt_3 twfe_alt_4 twfe_alt_5 twfe_alt_6,  se(
> a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  replace   scalar(N_clust M1 MDV)

--------------------------------------------------------------------------------
> ----------------------------
                      (1)             (2)             (3)             (4)       
>       (5)             (6)   
                  croppct         croppct           AgDum           AgDum       
>    devpct          devpct   
--------------------------------------------------------------------------------
> ----------------------------
post                0.221***        0.349***      0.00602***      0.00915***    
>   -0.0522         -0.0766   
                 (0.0676)        (0.0675)       (0.00131)       (0.00151)       
>   (0.163)         (0.117)   

offrespop                    -0.000000168***                    -7.76e-12       
>               0.000000138   
                               (5.15e-08)                      (7.44e-10)       
>              (0.000000113)   

has_casino                         -0.169***                      -0.0104***    
>                   -0.0665   
                                 (0.0548)                       (0.00128)       
>                   (0.210)   

has_credit                         0.0410                         0.00126       
>                     0.263*  
                                 (0.0712)                       (0.00135)       
>                   (0.141)   

_cons               6.428***        6.485***        0.144***        0.145***    
>     1.358***        1.183***
                 (0.0136)        (0.0332)      (0.000264)      (0.000635)       
>  (0.0329)        (0.0649)   
--------------------------------------------------------------------------------
> ----------------------------
N                 1410185         1410182         1410185         1410182       
>   1410185         1410182   
adj. R-sq           0.980           0.980           0.948           0.948       
>     0.888           0.888   
N_clust              2631            2631            2631            2631       
>      2631            2631   
M1                                                                              
>                             
MDV                                                                             
>                             
--------------------------------------------------------------------------------
> ----------------------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. 
end of do-file

. 
. *Table A4: The Impact of Winters Settlements, Additional Robustness
. do "run_table_A4.do"

. *This file runs the regressions for Table A4
. 
. 
. 
. 
. **Table A4, Panel A: Additional Robustness, did_multiplegt estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. preserve

. keep if yr ==5 //This keeps only observations where a full panel of well obser
> vations is available
(482,038 observations deleted)

. 
. *No rez-t controls; groundwater sample
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10)   robust_dynamic longdiff_placebo 
> covariances average_effect graphoptions (ytitle(Agriculture (%)) graphregion(c
> olor(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpattern(
> dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4292697   .0861552   .2604055   .5981338     467834      95065 
    Effect_1 |  .8689896   .1020309   .6690091    1.06897     276471      67163 
    Effect_2 |  1.336635   .3348241   .6803796    1.99289     113010      22307 
     Average |  .6989936   .1067881   .4896889   .9082983     857315     184535 
   Placebo_1 | -.1129123   .0677771  -.2457554   .0199308     467834      95065 
   Placebo_2 | -.2448079   .0733431  -.3885603  -.1010554     135559      44856 

. 
. 
. *all rez-t controls;  groundwater sample
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_
> credit ) robust_dynamic longdiff_placebo covariances average_effect graphoptio
> ns (ytitle(Agriculture (%)) xtitle(Time to Treatment) graphregion(color(white)
> )  ysize(15) xsize(20) xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4284651   .0773739   .2768122    .580118     467834      95065 
    Effect_1 |  .8729336   .0772788   .7214671     1.0244     276471      67163 
    Effect_2 |  1.350528   .2761607   .8092531   1.891803     113010      22307 
     Average |   .701694   .0846791   .5357229   .8676651     857315     184535 
   Placebo_1 | -.1140067   .0699433  -.2510954   .0230821     467834      95065 
   Placebo_2 |  -.245555   .0731187  -.3888677  -.1022423     135559      44856 

. 
. *No rez-t controls; groundwater control
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(med_depth) robust_dynamic
>  longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture 
> (%)) xtitle(Time to Treatment) graphregion(color(white))  ysize(15) xsize(20) 
> xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4282436   .0841118   .2633845   .5931028     467834      95065 
    Effect_1 |  .8700463   .1060992   .6620918   1.078001     276471      67163 
    Effect_2 |  1.335554   .3327714   .6833224   1.987786     113010      22307 
     Average |   .698719   .1059636   .4910304   .9064076     857315     184535 
   Placebo_1 | -.1131756   .0671157  -.2447224   .0183712     467834      95065 
   Placebo_2 | -.2421645   .0712645   -.381843  -.1024861     135559      44856 

. 
. 
. *all rez-t controls;  groundwater control
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_
> credit med_depth) robust_dynamic longdiff_placebo covariances average_effect g
> raphoptions (ytitle(Agriculture (%)) xtitle(Time to Treatment) graphregion(col
> or(white))  ysize(15) xsize(20) xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4307882   .0810905   .2718508   .5897257     467834      95065 
    Effect_1 |  .8801633   .1024624    .679337    1.08099     276471      67163 
    Effect_2 |  1.366008    .295941   .7859636   1.946052     113010      22307 
     Average |  .7073933    .098656   .5140275   .9007592     857315     184535 
   Placebo_1 |  -.115601   .0673467  -.2476005   .0163985     467834      95065 
   Placebo_2 | -.2425716   .0713945  -.3825049  -.1026383     135559      44856 

. 
. restore

. 
. *No rez-t controls; start date-by-year FE
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(STAR
> TDUM) cluster(TOWNSHIP) breps(10) seed(10)   robust_dynamic longdiff_placebo c
> ovariances average_effect graphoptions (ytitle(Agriculture (%)) graphregion(co
> lor(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpattern(d
> ash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |   .190273   .0610112    .070691    .309855     995583     151816 
    Effect_1 |  .3867619   .1037109   .1834885   .5900353     664347     102411 
    Effect_2 |  .2527163   .1850829  -.1100462   .6154789     339795      29948 
     Average |  .2676643   .0806983   .1094957    .425833    1999725     284175 
   Placebo_1 |  .0518475   .0533695  -.0527567   .1564517     713546     151610 
   Placebo_2 |  .2109624   .0716013   .0706238    .351301     202684      72463 

. 
. 
. *all rez-t controls;  start date-by-year FE
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(STAR
> TDUM) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_c
> redit ) robust_dynamic longdiff_placebo covariances average_effect graphoption
> s (ytitle(Agriculture (%)) xtitle(Time to Treatment) graphregion(color(white))
>   ysize(15) xsize(20) xline(-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |   .273753   .0658878    .144613    .402893     995577     151816 
    Effect_1 |  .5985879   .1108792   .3812646   .8159111     664338     102411 
    Effect_2 |  1.111502   .2458708   .6295957   1.593409     339789      29948 
     Average |  .4791039   .0894573   .3037676   .6544402    1999704     284175 
   Placebo_1 |  -.031216   .0492854  -.1278155   .0653834     713537     151610 
   Placebo_2 |  .1209867   .0701876  -.0165809   .2585543     202681      72463 

. 
. 
. 
. **Table A4, Panel B: Additional Robustness, csdid estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. 
. preserve

. *keep if dropthis ==0  //Drops 206 observations that appear only in 2012 due t
> o GIS processing issues (creates a balanced panel, other estimates do this aut
> omatically)
. 
. eststo clear

. *Baseline with no rezxt controls: groundwater sample
. csdid agpct if yr ==5, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple
> ) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 928,840
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 1,855 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .3811679   .0732122     5.21   0.000     .2376746    .5246611
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls: groundwater sample
. csdid agpct offrespop has_casino has_credit  if yr ==5, ivar(ID) time(t) gvar(
> TG)  cluster(TOWNSHIP) agg(simple) drimp
......xxxxxx
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 689,050
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 1,855 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |    4.34264   .9315807     4.66   0.000     2.516775    6.168505
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. *Baseline with no rezxt controls: groundwater control
. csdid agpct med_depth if yr ==5, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) 
> agg(simple) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 928,840
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 1,855 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .5471264   .1019858     5.36   0.000     .3472379     .747015
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. 
. restore

. 
. **Table A4, Panel C: Additional Robustness, twfe estimator
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. preserve

. 
. keep if yr ==5
(482,038 observations deleted)

. eststo clear

. *Baseline with no rezxt controls: groundwater sample
. reghdfe agpct post , absorb(ID stateXyear ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    928,840
Absorbing 2 HDFE groups                           F(   1,   1854) =      26.07
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9778
                                                  Adj R-squared   =     0.9722
                                                  Within R-sq.    =     0.0012
Number of clusters (TOWNSHIP) =      1,855        Root MSE        =     2.7885

                           (Std. err. adjusted for 1,855 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .4331838   .0848397     5.11   0.000     .2667924    .5995752
       _cons |   3.522766   .0168553   209.00   0.000     3.489709    3.555823
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    185768      185768           0    *|
  stateXyear |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_1

. 
. *all rez-t controls: Y=  groundwater sample
. reghdfe agpct post offrespop has_c* ,  absorb(ID stateXyear ) cluster(TOWNSHIP
> )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    928,840
Absorbing 2 HDFE groups                           F(   4,   1854) =      17.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9778
                                                  Adj R-squared   =     0.9723
                                                  Within R-sq.    =     0.0026
Number of clusters (TOWNSHIP) =      1,855        Root MSE        =     2.7865

                           (Std. err. adjusted for 1,855 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .7835692   .1026544     7.63   0.000     .5822389    .9848995
   offrespop |  -9.23e-08   4.48e-08    -2.06   0.040    -1.80e-07   -4.43e-09
  has_casino |  -.4817414   .0942799    -5.11   0.000    -.6666473   -.2968355
  has_credit |  -.2416253   .1020127    -2.37   0.018    -.4416972   -.0415535
       _cons |   3.687423   .0676132    54.54   0.000     3.554817    3.820029
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    185768      185768           0    *|
  stateXyear |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_2

. 
. *Baseline with no rezxt controls: groundwater control
. reghdfe agpct post med_depth, absorb(ID stateXyear ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    928,840
Absorbing 2 HDFE groups                           F(   2,   1854) =      13.05
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9778
                                                  Adj R-squared   =     0.9722
                                                  Within R-sq.    =     0.0012
Number of clusters (TOWNSHIP) =      1,855        Root MSE        =     2.7885

                           (Std. err. adjusted for 1,855 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .4400736   .0868039     5.07   0.000     .2698299    .6103173
   med_depth |  -.0001872    .000232    -0.81   0.420    -.0006422    .0002678
       _cons |   3.547399   .0320545   110.67   0.000     3.484533    3.610266
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    185768      185768           0    *|
  stateXyear |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_3

. 
. *all rez-t controls: Y=  groundwater control
. reghdfe agpct post offrespop has_c* med_depth,  absorb(ID stateXyear ) cluster
> (TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =    928,840
Absorbing 2 HDFE groups                           F(   5,   1854) =      13.97
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9778
                                                  Adj R-squared   =     0.9723
                                                  Within R-sq.    =     0.0026
Number of clusters (TOWNSHIP) =      1,855        Root MSE        =     2.7865

                           (Std. err. adjusted for 1,855 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .7845388   .1016829     7.72   0.000     .5851137    .9839638
   offrespop |  -9.29e-08   4.34e-08    -2.14   0.032    -1.78e-07   -7.81e-09
  has_casino |  -.4807565   .0972754    -4.94   0.000    -.6715373   -.2899757
  has_credit |   -.241172   .1016106    -2.37   0.018    -.4404551   -.0418888
   med_depth |  -.0000306   .0002326    -0.13   0.895    -.0004867    .0004255
       _cons |   3.691398   .0732823    50.37   0.000     3.547674    3.835123
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    185768      185768           0    *|
  stateXyear |        40           0          40     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_4

. 
. 
. restore

. 
. 
. *Baseline with no rezxt controls: start date-by-year FE
. reghdfe agpct post , absorb(ID startXyear ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   1,   2630) =      12.49
Statistics robust to heteroskedasticity           Prob > F        =     0.0004
                                                  R-squared       =     0.9832
                                                  Adj R-squared   =     0.9790
                                                  Within R-sq.    =     0.0004
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5529

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2431126   .0687811     3.53   0.000      .108242    .3779832
       _cons |   8.097518   .0138706   583.79   0.000      8.07032    8.124716
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  startXyear |        35           0          35     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_5

. 
. *all rez-t controls: start date-by-year FE
. reghdfe agpct post offrespop has_c* ,  absorb(ID startXyear ) cluster(TOWNSHIP
> )
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   4,   2630) =      26.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9791
                                                  Within R-sq.    =     0.0030
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5482

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3987778   .0669279     5.96   0.000      .267541    .5300145
   offrespop |  -5.40e-07   6.17e-08    -8.76   0.000    -6.61e-07   -4.20e-07
  has_casino |  -.1775345   .0659189    -2.69   0.007    -.3067926   -.0482764
  has_credit |   .2022894   .0807828     2.50   0.012      .043885    .3606937
       _cons |   8.223721   .0481558   170.77   0.000     8.129294    8.318148
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  startXyear |        35           0          35     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_alt_6

. 
. esttab twfe_alt_1 twfe_alt_2 twfe_alt_3 twfe_alt_4 twfe_alt_5 twfe_alt_6 ,  se
> (a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  replace   scalar(N_clust M1 MDV)

--------------------------------------------------------------------------------
> ----------------------------
                      (1)             (2)             (3)             (4)       
>       (5)             (6)   
                    agpct           agpct           agpct           agpct       
>     agpct           agpct   
--------------------------------------------------------------------------------
> ----------------------------
post                0.433***        0.784***        0.440***        0.785***    
>     0.243***        0.399***
                 (0.0848)         (0.103)        (0.0868)         (0.102)       
>  (0.0688)        (0.0669)   

offrespop                       -9.23e-08**                     -9.29e-08**     
>              -0.000000540***
                               (4.48e-08)                      (4.34e-08)       
>                (6.17e-08)   

has_casino                         -0.482***                       -0.481***    
>                    -0.178***
                                 (0.0943)                        (0.0973)       
>                  (0.0659)   

has_credit                         -0.242**                        -0.241**     
>                     0.202** 
                                  (0.102)                         (0.102)       
>                  (0.0808)   

med_depth                                       -0.000187      -0.0000306       
>                             
                                               (0.000232)      (0.000233)       
>                             

_cons               3.523***        3.687***        3.547***        3.691***    
>     8.098***        8.224***
                 (0.0169)        (0.0676)        (0.0321)        (0.0733)       
>  (0.0139)        (0.0482)   
--------------------------------------------------------------------------------
> ----------------------------
N                  928840          928840          928840          928840       
>   1410185         1410182   
adj. R-sq           0.972           0.972           0.972           0.972       
>     0.979           0.979   
N_clust              1855            1855            1855            1855       
>      2631            2631   
M1                                                                              
>                             
MDV                                                                             
>                             
--------------------------------------------------------------------------------
> ----------------------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. 
. 
end of do-file

. 
. *Table A5: Differential Impacts by 1974 Land Use
. do "run_table_A5.do"

. *This file runs the regressions for Table A5
. 
. 
. foreach p in "5" "10" "20"{
  2. 
. 
. *TWFE AG 1974 AG EFFECT
. ******************************************************************************
> *
. 
. 
. *TWFE Estimator Y = %AG, main table
. 
. eststo clear
  3. *Baseline with no rezxt controls
. reghdfe agpct post Ag`p'_post , absorb(ID stateXyear) cluster(TOWNSHIP)
  4. sum agpct if e(sample) ==1
  5. estadd scalar MDV = r(mean)
  6. est sto twfe_ag_ag74_`p'_1
  7. 
. *off-rez population
. reghdfe agpct post  Ag`p'_post offrespop , absorb(ID stateXyear) cluster(TOWNS
> HIP)
  8. sum agpct if e(sample) ==1
  9. estadd scalar MDV = r(mean)
 10. lincom  post + Ag`p'_post
 11. est sto twfe_ag_ag74_`p'_2
 12. 
. *casinos
. reghdfe agpct post  Ag`p'_post has_casino, absorb(ID stateXyear) cluster(TOWNS
> HIP)
 13. sum agpct if e(sample) ==1
 14. estadd scalar MDV = r(mean)
 15. est sto twfe_ag_ag74_`p'_3
 16. 
. *credit
. reghdfe agpct post Ag`p'_post has_credit, absorb(ID stateXyear) cluster(TOWNSH
> IP)
 17. sum agpct if e(sample) ==1
 18. estadd scalar MDV = r(mean)
 19. 
. est sto twfe_ag_ag74_`p'_4
 20. 
. *all rez-t controls
. reghdfe agpct post Ag`p'_post offrespop has_casino has_credit, absorb(ID state
> Xyear) cluster(TOWNSHIP)
 21. sum agpct if e(sample) ==1
 22. estadd scalar MDV = r(mean)
 23. est sto twfe_ag_ag74_`p'_5
 24. 
. 
. 
. }
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   2,   2630) =       5.28
Statistics robust to heteroskedasticity           Prob > F        =     0.0052
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0002
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5420

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1597794   .0570846     2.80   0.005      .047844    .2717147
    Ag5_post |   .1350779   .1459173     0.93   0.355    -.1510465    .4212022
       _cons |   8.104565   .0136036   595.76   0.000      8.07789     8.13124
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   3,   2630) =       8.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0004
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5417

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2491295   .0687499     3.62   0.000     .1143201    .3839389
    Ag5_post |   .1124572   .1476061     0.76   0.446    -.1769786     .401893
   offrespop |  -1.79e-07   6.17e-08    -2.91   0.004    -3.00e-07   -5.84e-08
       _cons |   8.163257   .0267921   304.69   0.000     8.110721    8.215793
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

 ( 1)  post + Ag5_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3615867   .1331386     2.72   0.007     .1005198    .6226536
------------------------------------------------------------------------------
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =      40.58
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0010
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3475569   .0637698     5.45   0.000     .2225127     .472601
    Ag5_post |   .0004127    .155605     0.00   0.998    -.3047079    .3055334
  has_casino |  -.4732365   .0633471    -7.47   0.000    -.5974517   -.3490213
       _cons |   8.172877   .0192003   425.66   0.000     8.135228    8.210527
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =       3.77
Statistics robust to heteroskedasticity           Prob > F        =     0.0102
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5420

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1679846   .0592438     2.84   0.005     .0518154    .2841538
    Ag5_post |   .0933172   .1605652     0.58   0.561    -.2215297    .4081641
  has_credit |  -.1016635   .0912546    -1.11   0.265    -.2806016    .0772745
       _cons |   8.158573    .050505   161.54   0.000      8.05954    8.257607
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   5,   2630) =      25.41
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3658656   .0710428     5.15   0.000     .2265602     .505171
    Ag5_post |  -.0146507   .1645181    -0.09   0.929    -.3372488    .3079474
   offrespop |  -5.38e-08   5.66e-08    -0.95   0.342    -1.65e-07    5.72e-08
  has_casino |   -.443604   .0582468    -7.62   0.000    -.5578183   -.3293897
  has_credit |  -.0406923   .0873112    -0.47   0.641     -.211898    .1305134
       _cons |   8.207822   .0534314   153.61   0.000     8.103051    8.312594
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   2,   2630) =       5.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0034
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0002
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5421

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1801053    .056105     3.21   0.001      .070091    .2901197
   Ag10_post |     .10626   .1687988     0.63   0.529     -.224732     .437252
       _cons |    8.10371   .0143254   565.69   0.000      8.07562    8.131801
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   3,   2630) =       8.01
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0004
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5417

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2644018   .0644258     4.10   0.000     .1380715    .3907321
   Ag10_post |    .098448   .1673037     0.59   0.556    -.2296123    .4265083
   offrespop |  -1.82e-07   6.02e-08    -3.02   0.003    -3.00e-07   -6.37e-08
       _cons |   8.163288   .0269183   303.26   0.000     8.110505    8.216072
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

 ( 1)  post + Ag10_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .3628498   .1602476     2.26   0.024     .0486257    .6770739
------------------------------------------------------------------------------
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =      39.72
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0010
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3480324   .0588218     5.92   0.000     .2326908     .463374
   Ag10_post |  -.0010222   .1741195    -0.01   0.995    -.3424473    .3404029
  has_casino |  -.4733799   .0606139    -7.81   0.000    -.5922357   -.3545241
       _cons |   8.172903   .0197612   413.58   0.000     8.134154    8.211652
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =       4.16
Statistics robust to heteroskedasticity           Prob > F        =     0.0060
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5420

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1909037   .0585098     3.26   0.001     .0761739    .3056336
   Ag10_post |    .039665   .2005535     0.20   0.843    -.3535936    .4329237
  has_credit |  -.1077879   .1025652    -1.05   0.293    -.3089046    .0933287
       _cons |   8.161433   .0587771   138.85   0.000     8.046178    8.276687
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   5,   2630) =      25.14
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3665447   .0655696     5.59   0.000     .2379716    .4951179
   Ag10_post |  -.0241163   .1982552    -0.12   0.903    -.4128682    .3646357
   offrespop |  -5.28e-08   5.48e-08    -0.96   0.335    -1.60e-07    5.46e-08
  has_casino |  -.4440051   .0562651    -7.89   0.000    -.5543335   -.3336767
  has_credit |  -.0444832   .0975125    -0.46   0.648    -.2356921    .1467257
       _cons |   8.209743   .0606681   135.32   0.000     8.090781    8.328705
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   2,   2630) =       4.86
Statistics robust to heteroskedasticity           Prob > F        =     0.0079
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5420

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1573374   .0604172     2.60   0.009     .0388673    .2758075
   Ag20_post |    .228598   .2104641     1.09   0.278    -.1840939    .6412899
       _cons |   8.105028   .0134372   603.18   0.000     8.078679    8.131376
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   3,   2630) =       8.09
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0005
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5417

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2498704   .0673994     3.71   0.000     .1177092    .3820315
   Ag20_post |    .162692   .2167381     0.75   0.453    -.2623024    .5876865
   offrespop |  -1.72e-07   6.46e-08    -2.65   0.008    -2.98e-07   -4.48e-08
       _cons |   8.161006   .0281514   289.90   0.000     8.105805    8.216207
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

 ( 1)  post + Ag20_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .4125624   .1982383     2.08   0.038     .0238437    .8012811
------------------------------------------------------------------------------
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =      39.98
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3202829   .0616741     5.19   0.000     .1993481    .4412176
   Ag20_post |   .1092718   .2150551     0.51   0.611    -.3124226    .5309661
  has_casino |  -.4645705   .0595974    -7.80   0.000    -.5814331   -.3477079
       _cons |   8.171963   .0186173   438.94   0.000     8.135456    8.208469
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =       3.56
Statistics robust to heteroskedasticity           Prob > F        =     0.0137
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5419

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1628176   .0621772     2.62   0.009     .0408965    .2847387
   Ag20_post |   .1783774   .2391302     0.75   0.456     -.290525    .6472798
  has_credit |  -.0848703    .099744    -0.85   0.395    -.2804549    .1107143
       _cons |   8.150023   .0553101   147.35   0.000     8.041567    8.258478
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   5,   2630) =      25.31
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5406

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3407313   .0673522     5.06   0.000     .2086627    .4727999
   Ag20_post |   .0815869   .2414949     0.34   0.736    -.3919523    .5551261
   offrespop |  -5.19e-08   6.00e-08    -0.87   0.387    -1.70e-07    6.58e-08
  has_casino |  -.4385509   .0541975    -8.09   0.000     -.544825   -.3322768
  has_credit |  -.0243771   .0948845    -0.26   0.797    -.2104329    .1616787
       _cons |   8.198089   .0587676   139.50   0.000     8.082853    8.313324
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

. 
. 
. *Table A5, Panel A
. esttab twfe_ag_ag74_5*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  replac
> e   scalar(N_clust M1 MDV )

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                    agpct           agpct           agpct           agpct       
>     agpct   
--------------------------------------------------------------------------------
> ------------
post                0.160***        0.249***        0.348***        0.168***    
>     0.366***
                 (0.0571)        (0.0687)        (0.0638)        (0.0592)       
>  (0.0710)   

Ag5_post            0.135           0.112        0.000413          0.0933       
>   -0.0147   
                  (0.146)         (0.148)         (0.156)         (0.161)       
>   (0.165)   

offrespop                    -0.000000179***                                    
> -5.38e-08   
                               (6.17e-08)                                      (
> 5.66e-08)   

has_casino                                         -0.473***                    
>    -0.444***
                                                 (0.0633)                       
>  (0.0582)   

has_credit                                                         -0.102       
>   -0.0407   
                                                                 (0.0913)       
>  (0.0873)   

_cons               8.105***        8.163***        8.173***        8.159***    
>     8.208***
                 (0.0136)        (0.0268)        (0.0192)        (0.0505)       
>  (0.0534)   
--------------------------------------------------------------------------------
> ------------
N                 1410185         1410182         1410185         1410185       
>   1410182   
adj. R-sq           0.979           0.979           0.979           0.979       
>     0.979   
N_clust              2631            2631            2631            2631       
>      2631   
M1                                                                              
>             
MDV                 8.147           8.147           8.147           8.147       
>     8.147   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. *Table A5, Panel B
. esttab twfe_ag_ag74_10*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  repla
> ce   scalar(N_clust M1 MDV )

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                    agpct           agpct           agpct           agpct       
>     agpct   
--------------------------------------------------------------------------------
> ------------
post                0.180***        0.264***        0.348***        0.191***    
>     0.367***
                 (0.0561)        (0.0644)        (0.0588)        (0.0585)       
>  (0.0656)   

Ag10_post           0.106          0.0984        -0.00102          0.0397       
>   -0.0241   
                  (0.169)         (0.167)         (0.174)         (0.201)       
>   (0.198)   

offrespop                    -0.000000182***                                    
> -5.28e-08   
                               (6.02e-08)                                      (
> 5.48e-08)   

has_casino                                         -0.473***                    
>    -0.444***
                                                 (0.0606)                       
>  (0.0563)   

has_credit                                                         -0.108       
>   -0.0445   
                                                                  (0.103)       
>  (0.0975)   

_cons               8.104***        8.163***        8.173***        8.161***    
>     8.210***
                 (0.0143)        (0.0269)        (0.0198)        (0.0588)       
>  (0.0607)   
--------------------------------------------------------------------------------
> ------------
N                 1410185         1410182         1410185         1410185       
>   1410182   
adj. R-sq           0.979           0.979           0.979           0.979       
>     0.979   
N_clust              2631            2631            2631            2631       
>      2631   
M1                                                                              
>             
MDV                 8.147           8.147           8.147           8.147       
>     8.147   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. *Table A5, Panel C
. esttab twfe_ag_ag74_20*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  repla
> ce   scalar(N_clust M1 MDV )

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                    agpct           agpct           agpct           agpct       
>     agpct   
--------------------------------------------------------------------------------
> ------------
post                0.157***        0.250***        0.320***        0.163***    
>     0.341***
                 (0.0604)        (0.0674)        (0.0617)        (0.0622)       
>  (0.0674)   

Ag20_post           0.229           0.163           0.109           0.178       
>    0.0816   
                  (0.210)         (0.217)         (0.215)         (0.239)       
>   (0.241)   

offrespop                    -0.000000172***                                    
> -5.19e-08   
                               (6.46e-08)                                      (
> 6.00e-08)   

has_casino                                         -0.465***                    
>    -0.439***
                                                 (0.0596)                       
>  (0.0542)   

has_credit                                                        -0.0849       
>   -0.0244   
                                                                 (0.0997)       
>  (0.0949)   

_cons               8.105***        8.161***        8.172***        8.150***    
>     8.198***
                 (0.0134)        (0.0282)        (0.0186)        (0.0553)       
>  (0.0588)   
--------------------------------------------------------------------------------
> ------------
N                 1410185         1410182         1410185         1410185       
>   1410182   
adj. R-sq           0.979           0.979           0.979           0.979       
>     0.979   
N_clust              2631            2631            2631            2631       
>      2631   
M1                                                                              
>             
MDV                 8.147           8.147           8.147           8.147       
>     8.147   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
end of do-file

. 
. *Table A6: Differential Impacts for Reservations with BIA Projects
. do "run_table_A6.do"

. *This file runs the regressions for Table A6
. 
. 
. *Table A6, Panel A: Differential Impacts for Reservations with BIA projects, d
> id_multiplegt
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. *Part i): reservations with no bia project
. 
. 
. *Baseline with no rezxt controls
. did_multiplegt agpct ID  year alt_post_nobia, placebo(2) dynamic(2) trends_non
> param(StateCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_
> placebo covariances average_effect graphoptions (ytitle(Agriculture (%)) graph
> region(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, l
> pattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0060082    .056061  -.1158877   .1038713     904677      60910 
    Effect_1 |  -.171738   .0666071  -.3022878  -.0411882     594285      32349 
    Effect_2 | -.0579419   .2057837   -.461278   .3453942     325346      15499 
     Average | -.0627039   .0517814  -.1641956   .0387877    1824308     108758 
   Placebo_1 | -.0615609   .0855606  -.2292597   .1061378     622640      60704 
   Placebo_2 |  .2093754   .0738372   .0646545   .3540962     147071      16850 

. 
. *off-rez population
. did_multiplegt agpct ID  year alt_post_nobia, placebo(2) dynamic(2) trends_non
> param(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robu
> st_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(De
> velopment (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) 
> xsize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0321472   .0566356  -.0788586   .1431531     904671      60910 
    Effect_1 |  -.022599   .0916507  -.2022344   .1570365     594276      32349 
    Effect_2 |  .4283941   .3068512  -.1730341   1.029822     325340      15499 
     Average |  .0723323   .0778013  -.0801583   .2248229    1824287     108758 
   Placebo_1 | -.0966658    .082151  -.2576817   .0643501     622631      60704 
   Placebo_2 |  .2064799   .0750182   .0594442   .3535156     147068      16850 

. 
. *casinos
. did_multiplegt agpct ID  year alt_post_nobia, placebo(2) dynamic(2) trends_non
> param(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) rob
> ust_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(D
> evelopment (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment)
>  xsize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .1832366    .058887   .0678182   .2986551     904677      60910 
    Effect_1 |  .0704933   .0569085  -.0410474    .182034     594285      32349 
    Effect_2 |  .2615377   .2071402  -.1444571   .6675325     325346      15499 
     Average |  .1608608   .0485187   .0657642   .2559575    1824308     108758 
   Placebo_1 | -.0419921    .083728  -.2060991   .1221148     622640      60704 
   Placebo_2 |  .2372249   .0694251   .1011518    .373298     147071      16850 

. 
. *credit
. did_multiplegt agpct ID  year alt_post_nobia, placebo(2) dynamic(2) trends_non
> param(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) rob
> ust_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(D
> evelopment (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment)
>  xsize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0008158   .0622433  -.1228126   .1211811     904677      60910 
    Effect_1 | -.1835963   .0768892  -.3342992  -.0328935     594285      32349 
    Effect_2 | -.0834481   .2569492  -.5870686   .4201723     325346      15499 
     Average | -.0669579     .05717  -.1790112   .0450953    1824308     108758 
   Placebo_1 |  -.061997   .0858419  -.2302472   .1062531     622640      60704 
   Placebo_2 |  .2031335   .0631186   .0794211   .3268459     147071      16850 

. 
. *all rez-t controls
. did_multiplegt agpct ID  year alt_post_nobia, placebo(2) dynamic(2) trends_non
> param(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_c
> asino has_credit) robust_dynamic longdiff_placebo covariances average_effect g
> raphoptions (ytitle(Development (%)) graphregion(color(white))  ysize(15) xtit
> le(Time to Treatment) xsize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(o
> ff) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .2036258   .0575285   .0908699   .3163817     904671      60910 
    Effect_1 |  .1278558   .1100869  -.0879145   .3436262     594276      32349 
    Effect_2 |  .4788848   .3591494   -.225048   1.182818     325340      15499 
     Average |  .2203157    .083517   .0566223   .3840091    1824287     108758 
   Placebo_1 | -.0617069   .0799751  -.2184582   .0950443     622631      60704 
   Placebo_2 |  .2274508   .0599447   .1099591   .3449424     147068      16850 

. 
. 
. 
. *Part ii): reservations with  BIA project
. 
. *no rez-t controls
. did_multiplegt agpct ID  year alt_post_bia, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_pl
> acebo covariances average_effect graphoptions (ytitle(Development (%)) graphre
> gion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpa
> ttern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |   .505664   .0604954   .3870931    .624235     652842      90906 
    Effect_1 |  1.004405   .1206153   .7679995   1.240811     379909      70062 
    Effect_2 |   2.76131    .126451   2.513466   3.009154     144670      14449 
     Average |  .8906589   .0689094   .7555966   1.025721    1177421     175417 
   Placebo_1 |  .1302298   .0779552  -.0225623   .2830219     652842      90906 
   Placebo_2 |  .1189649   .0781065  -.0341239   .2720537     185834      55613 

. 
. *off-rez population
. did_multiplegt agpct ID  year alt_post_bia, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robust
> _dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Deve
> lopment (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xs
> ize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .5238548   .0576779   .4108061   .6369035     652836      90906 
    Effect_1 |  1.027366   .1192847   .7935682   1.261164     379903      70062 
    Effect_2 |  2.736133   .1275627    2.48611   2.986156     144667      14449 
     Average |  .9071827   .0670134   .7758363   1.038529    1177406     175417 
   Placebo_1 |   .119524   .0775658  -.0325048   .2715529     652833      90906 
   Placebo_2 |  .1133355   .0783835  -.0402962   .2669672     185831      55613 

. 
. *casinos
. did_multiplegt agpct ID  year alt_post_bia, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Dev
> elopment (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4601532   .0714081   .3201932   .6001132     652842      90906 
    Effect_1 |  .9209857   .1331003    .660109   1.181862     379909      70062 
    Effect_2 |  2.873749   .1409794    2.59743   3.150069     144670      14449 
     Average |  .8430175   .0785177   .6891228   .9969122    1177421     175417 
   Placebo_1 |  .0214323   .0700248  -.1158163   .1586808     652842      90906 
   Placebo_2 |  .0180129   .0695669  -.1183382   .1543641     185834      55613 

. 
. *credit
. did_multiplegt agpct ID  year alt_post_bia, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Dev
> elopment (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .5094747   .0672995   .3775676   .6413818     652842      90906 
    Effect_1 |  1.007207   .1258747   .7604928   1.253922     379909      70062 
    Effect_2 |  2.765411   .1253547   2.519716   3.011106     144670      14449 
     Average |  .8940905   .0760953   .7449437   1.043237    1177421     175417 
   Placebo_1 |  .1321216    .076219  -.0172677   .2815109     652842      90906 
   Placebo_2 |   .111589     .08494  -.0548935   .2780714     185834      55613 

. 
. *all rez-t controls
. did_multiplegt agpct ID  year alt_post_bia, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_cas
> ino has_credit) robust_dynamic longdiff_placebo covariances average_effect gra
> phoptions (ytitle(Development (%)) graphregion(color(white))  ysize(15) xtitle
> (Time to Treatment) xsize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off
> ) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4757094   .0768645   .3250551   .6263637     652836      90906 
    Effect_1 |  .9390666   .1390153   .6665967   1.211537     379903      70062 
    Effect_2 |  2.861733     .14139   2.584608   3.138857     144667      14449 
     Average |  .8573109   .0850737   .6905665   1.024055    1177406     175417 
   Placebo_1 |  .0214117   .0693997  -.1146117   .1574351     652833      90906 
   Placebo_2 |  .0094957    .074262  -.1360577   .1550492     185831      55613 

. 
. 
. 
. *Table A6, Panel B: Differential Impacts for Reservations with BIA projects, c
> sdid
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. *Part i): reservations with no BIA project
. 
. preserve

. *keep if dropthis ==0  
. 
. eststo clear

. *Baseline with no rezxt controls
. csdid agpct , ivar(ID) time(t) gvar(TG_NO_BIA)  cluster(TOWNSHIP) agg(simple) 
> drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.3561362   .0645952    -5.51   0.000    -.4827406   -.2295319
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid agpct offrespop, ivar(ID) time(t) gvar(TG_NO_BIA)  cluster(TOWNSHIP) agg
> (simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.2037444    .153658    -1.33   0.185    -.5049086    .0974198
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid agpct has_casino, ivar(ID) time(t) gvar(TG_NO_BIA)  cluster(TOWNSHIP) ag
> g(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.3118969    .064601    -4.83   0.000    -.4385125   -.1852812
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid agpct has_credit, ivar(ID) time(t) gvar(TG_NO_BIA)  cluster(TOWNSHIP) ag
> g(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.6629989   .1022946    -6.48   0.000    -.8634927   -.4625051
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid agpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG_NO_BIA) 
>  cluster(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
................
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.1208822   .1830514    -0.66   0.509    -.4796563     .237892
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. 
. 
. *Part ii): reservations with  BIA project
. 
. 
. eststo clear

. *Baseline with no rezxt controls
. csdid agpct , ivar(ID) time(t) gvar(TG_BIA)  cluster(TOWNSHIP) agg(simple) dri
> mp
............
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .6390482   .0822362     7.77   0.000     .4778683    .8002282
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid agpct offrespop, ivar(ID) time(t) gvar(TG_BIA)  cluster(TOWNSHIP) agg(si
> mple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
............
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .6341764   .0796724     7.96   0.000     .4780213    .7903315
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid agpct has_casino, ivar(ID) time(t) gvar(TG_BIA)  cluster(TOWNSHIP) agg(s
> imple) drimp
............
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .6329043   .0815783     7.76   0.000     .4730138    .7927947
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid agpct has_credit, ivar(ID) time(t) gvar(TG_BIA)  cluster(TOWNSHIP) agg(s
> imple) drimp
............
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,200
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .8330092   .1095938     7.60   0.000     .6182094    1.047809
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid agpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG_BIA)  cl
> uster(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
............
Difference-in-difference with Multiple Time Periods

                                                     Number of obs = 1,410,182
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .8798828   .1061911     8.29   0.000     .6717522    1.088013
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. restore

. 
. 
. 
. *Table A6, Panel C: Differential Impacts for Reservations with BIA projects, T
> WFE
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. eststo clear

. *Baseline with no rezxt controls
. reghdfe agpct post bia_post , absorb(ID stateXyear) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   2,   2630) =      15.27
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0008
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5410

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.0931803   .0691421    -1.35   0.178    -.2287588    .0423982
    bia_post |   .6152979    .114103     5.39   0.000     .3915572    .8390387
       _cons |   8.088797   .0147487   548.44   0.000     8.059877    8.117717
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_ag_bia_1

. 
. *off-rez population
. reghdfe agpct post  bia_post offrespop , absorb(ID stateXyear) cluster(TOWNSHI
> P)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   3,   2630) =      12.99
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0009
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5408

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.0098531   .0695758    -0.14   0.887    -.1462821    .1265758
    bia_post |   .5717316   .1153585     4.96   0.000      .345529    .7979343
   offrespop |  -1.37e-07   6.13e-08    -2.23   0.026    -2.57e-07   -1.67e-08
       _cons |   8.134764   .0282295   288.17   0.000      8.07941    8.190118
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_ag_bia_2

. 
. *casinos
. reghdfe agpct post  bia_post has_casino, absorb(ID stateXyear) cluster(TOWNSHI
> P)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =      42.46
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0013
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5401

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1063732   .0712426     1.49   0.136    -.0333239    .2460703
    bia_post |   .4388549   .1258288     3.49   0.000     .1921214    .6855884
  has_casino |  -.3859327   .0607844    -6.35   0.000    -.5051228   -.2667427
       _cons |   8.149161   .0210937   386.33   0.000     8.107799    8.190523
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_ag_bia_3

. 
. *credit
. reghdfe agpct post bia_post has_credit, absorb(ID stateXyear) cluster(TOWNSHIP
> )
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =      10.30
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0008
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5410

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.0934488   .0707039    -1.32   0.186    -.2320897    .0451921
    bia_post |   .6161027   .1167188     5.28   0.000     .3872327    .8449728
  has_credit |   .0018133   .0815481     0.02   0.982    -.1580917    .1617183
       _cons |   8.087812   .0449418   179.96   0.000     7.999687    8.175937
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_ag_bia_4

. 
. *all rez-t controls
. reghdfe agpct post bia_post offrespop has_casino has_credit, absorb(ID stateXy
> ear) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   5,   2630) =      26.84
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0013
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5401

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .1217217    .073711     1.65   0.099    -.0228157     .266259
    bia_post |   .4485846   .1262066     3.55   0.000     .2011103     .696059
   offrespop |  -5.29e-08   5.95e-08    -0.89   0.374    -1.70e-07    6.37e-08
  has_casino |  -.3640733   .0578609    -6.29   0.000    -.4775308   -.2506158
  has_credit |   .0373049   .0805834     0.46   0.643    -.1207084    .1953182
       _cons |   8.143237   .0486158   167.50   0.000     8.047908    8.238566
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. est sto twfe_ag_bia_5

. 
. 
. esttab twfe_ag_bia*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  replace  
>  scalar(N_clust M1 MDV diff pval)

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                    agpct           agpct           agpct           agpct       
>     agpct   
--------------------------------------------------------------------------------
> ------------
post              -0.0932        -0.00985           0.106         -0.0934       
>     0.122*  
                 (0.0691)        (0.0696)        (0.0712)        (0.0707)       
>  (0.0737)   

bia_post            0.615***        0.572***        0.439***        0.616***    
>     0.449***
                  (0.114)         (0.115)         (0.126)         (0.117)       
>   (0.126)   

offrespop                    -0.000000137**                                     
> -5.29e-08   
                               (6.13e-08)                                      (
> 5.95e-08)   

has_casino                                         -0.386***                    
>    -0.364***
                                                 (0.0608)                       
>  (0.0579)   

has_credit                                                        0.00181       
>    0.0373   
                                                                 (0.0815)       
>  (0.0806)   

_cons               8.089***        8.135***        8.149***        8.088***    
>     8.143***
                 (0.0147)        (0.0282)        (0.0211)        (0.0449)       
>  (0.0486)   
--------------------------------------------------------------------------------
> ------------
N                 1410185         1410182         1410185         1410185       
>   1410182   
adj. R-sq           0.979           0.979           0.979           0.979       
>     0.979   
N_clust              2631            2631            2631            2631       
>      2631   
M1                                                                              
>             
MDV                                                                             
>             
diff                                                                            
>             
pval                                                                            
>             
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. 
. 
end of do-file

. 
. *Table A7: Differential Impacts by Land Tenure Class: Agriculture
. do "run_table_A7.do"

. *This file runs the regressions for Table A7
. 
. *Table A7, Panel A: Differential Impacts Across Land Tenure Regimes, did_multi
> plegt
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. *Part i): Fee Simple
. 
. preserve

. keep if Fee ==1
(1,236,658 observations deleted)

. *Baseline with no rezxt controls
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_placebo co
> variances average_effect graphoptions (ytitle(Agriculture (%)) graphregion(col
> or(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpattern(da
> sh) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .5837338   .0717085   .4431851   .7242826     118944      23231 
    Effect_1 |  .9959356   .1257159   .7495324   1.242339      79602      18958 
    Effect_2 |  1.806278   .3693426   1.082367    2.53019      30378       2407 
     Average |   .824948   .0980615   .6327474   1.017149     228924      44596 
   Placebo_1 |  .0046454   .1315308  -.2531549   .2624458      83864      23220 
   Placebo_2 |  .2590787   .2557573  -.2422055    .760363      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *off-rez population
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robust_dynamic
>  longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture 
> (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) 
> yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .6078326   .0869958   .4373209   .7783443     118944      23231 
    Effect_1 |  1.072384   .1801505   .7192888   1.425479      79602      18958 
    Effect_2 |  1.924646   .4507108   1.041253   2.808039      30378       2407 
     Average |  .8763888   .1321594   .6173565   1.135421     228924      44596 
   Placebo_1 |  .0035571   .1309437  -.2530926   .2602068      83864      23220 
   Placebo_2 |  .2523242   .2554943  -.2484446   .7530929      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *casinos
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) robust_dynami
> c longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture
>  (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20)
>  yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4333752   .1015046   .2344261   .6323242     118944      23231 
    Effect_1 |  .7615397   .1621776   .4436715   1.079408      79602      18958 
    Effect_2 |  1.840542   .3705642   1.114236   2.566848      30378       2407 
     Average |  .6488294   .1285415    .396888   .9007707     228924      44596 
   Placebo_1 | -.2083383   .1022547  -.4087574  -.0079191      83864      23220 
   Placebo_2 |  .0224404   .2645154  -.4960097   .5408905      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *credit
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) robust_dynami
> c longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture
>  (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20)
>  yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .6007824   .0835722   .4369809   .7645839     118944      23231 
    Effect_1 |   1.00443   .1312101   .7472578   1.261601      79602      18958 
    Effect_2 |  1.860593   .4077491   1.061405   2.659781      30378       2407 
     Average |  .8403713    .109854   .6250575   1.055685     228924      44596 
   Placebo_1 |  .0077858   .1311252  -.2492195   .2647911      83864      23220 
   Placebo_2 |  .2017941    .202831  -.1957546   .5993428      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *all rez-t controls
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_
> credit) robust_dynamic longdiff_placebo covariances average_effect graphoption
> s (ytitle(Agriculture (%)) scheme(white_hue) ysize(20) xsize(20) xline(-.5) le
> gend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .4754361   .1293346   .2219403   .7289318     118944      23231 
    Effect_1 |  .8514648   .2246404   .4111697    1.29176      79602      18958 
    Effect_2 |  2.010662   .4301092   1.167648   2.853676      30378       2407 
     Average |  .7181494    .170868    .383248   1.053051     228924      44596 
   Placebo_1 | -.2037955   .1035998  -.4068512  -.0007398      83864      23220 
   Placebo_2 | -.0318491   .1939931  -.4120756   .3483774      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. restore

. 
. 
. *Part ii): Allotted Trust
. 
. 
. preserve

. keep if Allotted ==1
(1,264,533 observations deleted)

. *Baseline with no rezxt controls
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_placebo co
> variances average_effect graphoptions (ytitle(Agriculture (%)) graphregion(col
> or(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpattern(da
> sh) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .3562206   .1500542   .0621144   .6503268      73510      20655 
    Effect_1 |  .9009411   .3125544   .2883345   1.513548      38443      11961 
    Effect_2 |  2.890555   1.119949   .6954551   5.085655      12070       3176 
     Average |  .7631397   .2616442   .2503171   1.275962     124023      35792 
   Placebo_1 |   .866461   .1905989   .4928873   1.240035      73510      20655 
   Placebo_2 |  .7477341   .1447029   .4641164   1.031352      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *off-rez population
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robust_dynamic
>  longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture 
> (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) 
> yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .3789096   .1516649   .0816463   .6761729      73510      20655 
    Effect_1 |  .9037648   .3112915   .2936335   1.513896      38443      11961 
    Effect_2 |  3.086171   1.110093   .9103891   5.261952      12070       3176 
     Average |  .7945347   .2635519    .277973   1.311096     124023      35792 
   Placebo_1 |  .8457298   .1913381   .4707071   1.220753      73510      20655 
   Placebo_2 |  .7640478   .1449991   .4798496   1.048246      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *casinos
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) robust_dynami
> c longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture
>  (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20)
>  yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |   .177583   .1333461  -.0837754   .4389413      73510      20655 
    Effect_1 |  .7433377   .2799724   .1945918   1.292084      38443      11961 
    Effect_2 |  3.039681   1.109978    .864123   5.215238      12070       3176 
     Average |  .6206153   .2392233   .1517376   1.089493     124023      35792 
   Placebo_1 |  .4639738   .1402351    .189113   .7388346      73510      20655 
   Placebo_2 |  .4450303   .1381906   .1741767   .7158838      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *credit
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) robust_dynami
> c longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture
>  (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20)
>  yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .2845004   .1486054  -.0067661   .5757669      73510      20655 
    Effect_1 |  .8635304   .3079818   .2598861   1.467175      38443      11961 
    Effect_2 |  2.938806   1.102498   .7779107   5.099702      12070       3176 
     Average |  .7135307      .2607   .2025588   1.224503     124023      35792 
   Placebo_1 |  .8112105   .1921473   .4346018   1.187819      73510      20655 
   Placebo_2 |  .9134931   .1211555   .6760284   1.150958      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *all rez-t controls
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_
> credit) robust_dynamic longdiff_placebo covariances average_effect graphoption
> s (ytitle(Agriculture (%)) scheme(white_hue) ysize(20) xsize(20) xline(-.5) le
> gend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .1295666   .1358704  -.1367395   .3958726      73510      20655 
    Effect_1 |  .7173891    .277221   .1740359   1.260742      38443      11961 
    Effect_2 |  3.146953   1.100596   .9897844   5.304121      12070       3176 
     Average |  .5937531   .2410336   .1213272   1.066179     124023      35792 
   Placebo_1 |  .4228556    .137968   .1524382   .6932729      73510      20655 
   Placebo_2 |  .6068508   .1139646   .3834802   .8302214      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. restore

. 
. 
. 
. *Part iii): Tribal Trust
. 
. preserve

. keep if Tribal ==1
(489,733 observations deleted)

. *Baseline with no rezxt controls
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_placebo co
> variances average_effect graphoptions (ytitle(Agriculture (%)) graphregion(col
> or(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpattern(da
> sh) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .1579835   .0695086   .0217467   .2942204     653896      90911 
    Effect_1 |  .3195152   .1030005   .1176343   .5213962     439395      60457 
    Effect_2 |  1.117675   .3458502   .4398083   1.795541     239466      22378 
     Average |   .337796   .0969846   .1477061   .5278858    1332757     173746 
   Placebo_1 | -.0833692   .0738009   -.228019   .0612806     469668      90730 
   Placebo_2 |  .0284145   .0250416  -.0206671   .0774961     131396      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *off-rez population
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robust_dynamic
>  longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture 
> (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) 
> yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .1673343   .0694024   .0313057    .303363     653890      90911 
    Effect_1 |  .3442166   .1087973   .1309739   .5574592     439386      60457 
    Effect_2 |    1.1957    .352696    .504416   1.886984     239460      22378 
     Average |  .3613333   .0999675    .165397   .5572695    1332736     173746 
   Placebo_1 | -.0909147   .0723018  -.2326262   .0507968     469659      90730 
   Placebo_2 |  .0261156   .0249893  -.0228635   .0750947     131393      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *casinos
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) robust_dynami
> c longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture
>  (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20)
>  yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .2213275   .0744155   .0754731   .3671818     653896      90911 
    Effect_1 |  .3811634   .1029961   .1792911   .5830357     439395      60457 
    Effect_2 |  1.255771    .366666   .5371053   1.974436     239466      22378 
     Average |  .4101777   .1054364   .2035224    .616833    1332757     173746 
   Placebo_1 | -.0886674    .074723  -.2351246   .0577897     469668      90730 
   Placebo_2 |  .0241223   .0263911  -.0276044   .0758489     131396      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *credit
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) robust_dynami
> c longdiff_placebo covariances average_effect graphoptions (ytitle(Agriculture
>  (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20)
>  yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .1741847   .0829935   .0115175   .3368519     653896      90911 
    Effect_1 |  .3049267   .0955721   .1176054   .4922479     439395      60457 
    Effect_2 |  1.055566   .3026261   .4624189   1.648713     239466      22378 
     Average |  .3331974   .0964305   .1441936   .5222012    1332757     173746 
   Placebo_1 | -.0831026   .0736993  -.2275532   .0613479     469668      90730 
   Placebo_2 |  .0172854   .0239973  -.0297494   .0643202     131396      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *all rez-t controls
. did_multiplegt agpct ID  year post, placebo(2) dynamic(2) trends_nonparam(Stat
> eCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_casino has_
> credit) robust_dynamic longdiff_placebo covariances average_effect graphoption
> s (ytitle(Agriculture (%)) scheme(white_hue) ysize(20) xsize(20) xline(-.5) le
> gend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .2423647   .0906016   .0647855   .4199439     653890      90911 
    Effect_1 |  .3694434   .1014044   .1706908   .5681959     439386      60457 
    Effect_2 |  1.199271   .3198668    .572332    1.82621     239460      22378 
     Average |  .4098301   .1070516    .200009   .6196512    1332736     173746 
   Placebo_1 | -.0894653   .0731451  -.2328297   .0538991     469659      90730 
   Placebo_2 |  .0111862   .0254236   -.038644   .0610164     131393      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. restore

. 
. 
. 
. 
. 
. 
. 
. *Table A7, Panel B: Differential Impacts Across Land Tenure Regimes, csdid
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. *Part i): Fee Simple
. preserve

. keep if  Fee ==1
(1,236,658 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. csdid agpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : regression adjustment
Treatment model: none
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .7248844   .1902065     3.81   0.000     .3520864    1.097682
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid agpct offrespop, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple
> ) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .7209246   .1801969     4.00   0.000     .3677452    1.074104
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid agpct has_casino, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .9751082   .1560953     6.25   0.000      .669167    1.281049
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid agpct has_credit, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .9935595   .2376916     4.18   0.000     .5276926    1.459426
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid agpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  cluste
> r(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .8110429   .2363202     3.43   0.001     .3478639    1.274222
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. restore

. 
. 
. 
. *Part ii): Allotted Trust
. preserve

. keep if Allotted ==1
(1,264,533 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. csdid agpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : regression adjustment
Treatment model: none
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0071174   .2221376    -0.03   0.974    -.4424992    .4282644
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid agpct offrespop, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple
> ) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0467651   .1801223    -0.26   0.795    -.3997984    .3062681
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid agpct has_casino, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .1672032   .1755961     0.95   0.341    -.1769589    .5113653
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid agpct has_credit, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.2541116   .4418006    -0.58   0.565    -1.120025    .6118016
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid agpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  cluste
> r(TOWNSHIP) agg(simple) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0223646   .2793803    -0.08   0.936      -.56994    .5252109
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. restore

. 
. 
. 
. *Part iii): Tribal Trust
. preserve

. keep if Tribal ==1
(489,733 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. csdid agpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 921,140
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .1985639   .0444307     4.47   0.000     .1114814    .2856464
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid agpct offrespop, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple
> ) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
..............xx
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 860,229
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .5308998   .2156109     2.46   0.014     .1083103    .9534894
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid agpct has_casino, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 921,140
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .1995058   .0444786     4.49   0.000     .1123293    .2866824
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid agpct has_credit, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 921,140
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .2488218   .0977477     2.55   0.011     .0572397    .4404038
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid agpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  cluste
> r(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
............xxxx
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 768,867
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .5258877   .1509597     3.48   0.000     .2300121    .8217633
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. restore

. 
. 
. 
. *Table A7, Panel C: Differential Impacts Across Land Tenure Regimes, twfe
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. preserve 

. keep if tenure <=3
(166,108 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. reghdfe agpct post allotted_post tribal_post , absorb(ID stateXyear) cluster(T
> OWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,244,285
Absorbing 2 HDFE groups                           F(   3,   2582) =      13.32
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9836
                                                  Adj R-squared   =     0.9795
                                                  Within R-sq.    =     0.0014
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.4955

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
        agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |   .9080689   .1475036     6.16   0.000     .6188315    1.197306
allotted_post |   -.727401   .1613888    -4.51   0.000    -1.043866   -.4109364
  tribal_post |  -.9054804   .1483176    -6.11   0.000    -1.196314    -.614647
        _cons |    7.91488   .0125454   630.90   0.000      7.89028     7.93948
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,244,285    7.952992    24.42777          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1806679   .1398396     1.29   0.196    -.0935412     .454877
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0025884   .0588682     0.04   0.965    -.1128452    .1180221
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_tenure_1

. 
. *off-rez population
. reghdfe agpct post  allotted_post tribal_post offrespop , absorb(ID stateXyear
> ) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,244,282
Absorbing 2 HDFE groups                           F(   4,   2582) =      10.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9836
                                                  Adj R-squared   =     0.9795
                                                  Within R-sq.    =     0.0014
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.4954

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
        agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |   .9276865   .1475145     6.29   0.000     .6384278    1.216945
allotted_post |  -.7284205   .1613216    -4.52   0.000    -1.044753   -.4120877
  tribal_post |  -.8869065   .1500174    -5.91   0.000    -1.181073   -.5927398
    offrespop |  -6.90e-08   4.74e-08    -1.46   0.145    -1.62e-07    2.39e-08
        _cons |   7.937439   .0203977   389.13   0.000     7.897442    7.977436
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,244,282    7.953012    24.42779          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1992661   .1370829     1.45   0.146    -.0695375    .4680697
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0407801   .0659659     0.62   0.536    -.0885714    .1701316
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_tenure_2

. 
. *casinos
. reghdfe agpct post  allotted_post tribal_post has_casino, absorb(ID stateXyear
> ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,244,285
Absorbing 2 HDFE groups                           F(   4,   2582) =      29.52
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9836
                                                  Adj R-squared   =     0.9795
                                                  Within R-sq.    =     0.0019
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.4947

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
        agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |   .9470236   .1460268     6.49   0.000     .6606821    1.233365
allotted_post |  -.7046807    .161076    -4.37   0.000    -1.020532   -.3888294
  tribal_post |  -.8135234   .1535578    -5.30   0.000    -1.114632   -.5124145
   has_casino |  -.3491376   .0547625    -6.38   0.000    -.4565205   -.2417548
        _cons |   7.964383   .0167012   476.87   0.000     7.931634    7.997132
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,244,285    7.952992    24.42777          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2423429   .1357994     1.78   0.074    -.0239438    .5086296
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1335002   .0606751     2.20   0.028     .0145235    .2524769
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_tenure_3

. 
. *credit
. reghdfe agpct post  allotted_post tribal_post has_credit, absorb(ID stateXyear
> ) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,244,285
Absorbing 2 HDFE groups                           F(   4,   2582) =      10.00
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9836
                                                  Adj R-squared   =     0.9795
                                                  Within R-sq.    =     0.0014
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.4955

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
        agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |   .9053852   .1472806     6.15   0.000     .6165852    1.194185
allotted_post |  -.7250419   .1586445    -4.57   0.000    -1.036125   -.4139586
  tribal_post |  -.9028818   .1508727    -5.98   0.000    -1.198726    -.607038
   has_credit |  -.0102583   .0765962    -0.13   0.893    -.1604545     .139938
        _cons |   7.920333   .0397892   199.06   0.000     7.842311    7.998355
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,244,285    7.952992    24.42777          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1803433   .1401692     1.29   0.198    -.0945122    .4551988
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0025034   .0586269     0.04   0.966     -.112457    .1174638
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_tenure_4

. 
. *all rez-t controls
. reghdfe agpct post  allotted_post tribal_post offrespop has_casino has_credit,
>  absorb(ID stateXyear) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,244,282
Absorbing 2 HDFE groups                           F(   6,   2582) =      21.34
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9836
                                                  Adj R-squared   =     0.9795
                                                  Within R-sq.    =     0.0019
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.4947

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
        agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |   .9528825   .1457551     6.54   0.000     .6670737    1.238691
allotted_post |  -.7117221   .1590528    -4.47   0.000    -1.023606    -.399838
  tribal_post |  -.8236579   .1550002    -5.31   0.000    -1.127595   -.5197206
    offrespop |   1.70e-08   4.55e-08     0.37   0.708    -7.21e-08    1.06e-07
   has_casino |   -.362071    .056391    -6.42   0.000    -.4726472   -.2514949
   has_credit |   .0353722   .0770808     0.46   0.646    -.1157743    .1865186
        _cons |   7.941874   .0414158   191.76   0.000     7.860662    8.023085
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,244,282    7.953012    24.42779          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2411604   .1361543     1.77   0.077    -.0258223    .5081431
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1292246   .0635637     2.03   0.042     .0045836    .2538656
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_tenure_5

. 
. 
. esttab twfe_ag_tenure_*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  repla
> ce   scalar(N_clust M1 MDV adiff apval tdiff tpval)

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                    agpct           agpct           agpct           agpct       
>     agpct   
--------------------------------------------------------------------------------
> ------------
post                0.908***        0.928***        0.947***        0.905***    
>     0.953***
                  (0.148)         (0.148)         (0.146)         (0.147)       
>   (0.146)   

allotted_p~t       -0.727***       -0.728***       -0.705***       -0.725***    
>    -0.712***
                  (0.161)         (0.161)         (0.161)         (0.159)       
>   (0.159)   

tribal_post        -0.905***       -0.887***       -0.814***       -0.903***    
>    -0.824***
                  (0.148)         (0.150)         (0.154)         (0.151)       
>   (0.155)   

offrespop                       -6.90e-08                                       
>  1.70e-08   
                               (4.74e-08)                                      (
> 4.55e-08)   

has_casino                                         -0.349***                    
>    -0.362***
                                                 (0.0548)                       
>  (0.0564)   

has_credit                                                        -0.0103       
>    0.0354   
                                                                 (0.0766)       
>  (0.0771)   

_cons               7.915***        7.937***        7.964***        7.920***    
>     7.942***
                 (0.0125)        (0.0204)        (0.0167)        (0.0398)       
>  (0.0414)   
--------------------------------------------------------------------------------
> ------------
N                 1244285         1244282         1244285         1244285       
>   1244282   
adj. R-sq           0.980           0.980           0.980           0.980       
>     0.980   
N_clust              2583            2583            2583            2583       
>      2583   
M1                                                                              
>             
MDV                 7.953           7.953           7.953           7.953       
>     7.953   
adiff               0.181           0.199           0.242           0.180       
>     0.241   
apval               0.196           0.146          0.0743           0.198       
>    0.0765   
tdiff             0.00259          0.0408           0.134         0.00250       
>     0.129   
tpval               0.965           0.536          0.0278           0.966       
>    0.0421   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. restore

. 
. 
. 
end of do-file

. 
. *Table A8: Differential Impacts by Land Tenure Class: Development
. do "run_table_A8.do"

. *This file runs the regressions for Table A8
. 
. *Table A8, Panel A: Differential Development Impacts Across Land Tenure Regime
> s, did_multiplegt
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. *Part i): Fee Simple
. 
. preserve

. keep if Fee ==1
(1,236,658 observations deleted)

. *Baseline with no rezxt controls
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_pl
> acebo covariances average_effect graphoptions (ytitle(Agriculture (%)) graphre
> gion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpa
> ttern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0270067   .0178673  -.0620267   .0080133     118944      23231 
    Effect_1 | -.0496187   .0314546  -.1112696   .0120322      79602      18958 
    Effect_2 |  .1018801   .2100389  -.3097961   .5135564      30378       2407 
     Average | -.0296627   .0272566  -.0830856   .0237602     228924      44596 
   Placebo_1 |  .0370379   .0317458   -.025184   .0992597      83864      23220 
   Placebo_2 |  .0103842   .0514813  -.0905191   .1112876      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *off-rez population
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robust
> _dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agri
> culture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xs
> ize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0935969   .0598321  -.2108678   .0236741     118944      23231 
    Effect_1 | -.2608621   .1725056  -.5989732   .0772489      79602      18958 
    Effect_2 | -.2251963   .2884798  -.7906166   .3402241      30378       2407 
     Average | -.1718051   .1188447  -.4047406   .0611304     228924      44596 
   Placebo_1 |  .0400453    .030147  -.0190428   .0991334      83864      23220 
   Placebo_2 |  .0290486   .0573498   -.083357   .1414541      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *casinos
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agr
> iculture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0378196   .0199784  -.0769773   .0013381     118944      23231 
    Effect_1 |  -.066475   .0390738  -.1430596   .0101096      79602      18958 
    Effect_2 |  .1043442    .209279  -.3058427   .5145311      30378       2407 
     Average | -.0423281   .0328671  -.1067477   .0220915     228924      44596 
   Placebo_1 |  .0217213   .0257409  -.0287308   .0721735      83864      23220 
   Placebo_2 | -.0066334   .0525293  -.1095908   .0963241      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *credit
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agr
> iculture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0183671   .0213655  -.0602436   .0235093     118944      23231 
    Effect_1 | -.0453142   .0318843  -.1078075   .0171791      79602      18958 
    Effect_2 |   .129405   .1881377  -.2393449    .498155      30378       2407 
     Average | -.0218467   .0263252  -.0734441   .0297506     228924      44596 
   Placebo_1 |  .0386293    .031546   -.023201   .1004595      83864      23220 
   Placebo_2 | -.0186455   .0554185  -.1272657   .0899747      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *all rez-t controls
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_cas
> ino has_credit) robust_dynamic longdiff_placebo covariances average_effect gra
> phoptions (ytitle(Agriculture (%)) scheme(white_hue) ysize(20) xsize(20) xline
> (-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.1008244   .0573543  -.2132388     .01159     118944      23231 
    Effect_1 | -.2755484   .1665849  -.6020547   .0509579      79602      18958 
    Effect_2 | -.2091441   .2924669  -.7823792   .3640909      30378       2407 
     Average | -.1809469   .1153185  -.4069713   .0450774     228924      44596 
   Placebo_1 |  .0247371   .0251431  -.0245435   .0740176      83864      23220 
   Placebo_2 | -.0013114   .0700981  -.1387036   .1360808      28400      16551 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. restore

. 
. 
. *Part ii): Allotted Trust
. 
. 
. preserve

. keep if Allotted ==1
(1,264,533 observations deleted)

. *Baseline with no rezxt controls
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_pl
> acebo covariances average_effect graphoptions (ytitle(Agriculture (%)) graphre
> gion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpa
> ttern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0151974   .0093044  -.0030393   .0334341      73510      20655 
    Effect_1 |  .1023666   .0763646   -.047308   .2520412      38443      11961 
    Effect_2 |   .541025   .3313754  -.1084707   1.190521      12070       3176 
     Average |  .0909869    .057399  -.0215151   .2034889     124023      35792 
   Placebo_1 | -.0343634   .0317819   -.096656   .0279291      73510      20655 
   Placebo_2 | -.1016108   .1704212  -.4356364   .2324148      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *off-rez population
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robust
> _dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agri
> culture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xs
> ize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0062964   .0068283  -.0196799   .0070871      73510      20655 
    Effect_1 |  .0996916   .0724533   -.042317   .2417002      38443      11961 
    Effect_2 |  .3557139   .2677327  -.1690421     .88047      12070       3176 
     Average |  .0612457   .0493297  -.0354405    .157932     124023      35792 
   Placebo_1 | -.0147243   .0277418  -.0690983   .0396497      73510      20655 
   Placebo_2 | -.1170652   .1726951  -.4555476   .2214172      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *casinos
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agr
> iculture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |    .02151   .0081969   .0054441    .037576      73510      20655 
    Effect_1 |   .107936   .0757393  -.0405131    .256385      38443      11961 
    Effect_2 |  .5357552   .3305597  -.1121418   1.183652      12070       3176 
     Average |  .0960234   .0568765  -.0154545   .2075014     124023      35792 
   Placebo_1 | -.0201404   .0321648  -.0831835   .0429027      73510      20655 
   Placebo_2 | -.0909139   .1705422  -.4251767   .2433489      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *credit
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agr
> iculture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0233092   .0090468   .0055774    .041041      73510      20655 
    Effect_1 |  .1065979   .0744915  -.0394054   .2526012      38443      11961 
    Effect_2 |  .5355676   .3307393  -.1126814   1.183817      12070       3176 
     Average |  .0965979   .0559471  -.0130585   .2062543     124023      35792 
   Placebo_1 | -.0281144   .0344568  -.0956497    .039421      73510      20655 
   Placebo_2 | -.1203588   .1677305  -.4491105   .2083929      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *all rez-t controls
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_cas
> ino has_credit) robust_dynamic longdiff_placebo covariances average_effect gra
> phoptions (ytitle(Agriculture (%)) scheme(white_hue) ysize(20) xsize(20) xline
> (-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |   .001119   .0077572  -.0140851   .0163231      73510      20655 
    Effect_1 |  .1034758   .0699323  -.0335915   .2405431      38443      11961 
    Effect_2 |  .3514035   .2675575  -.1730091   .8758162      12070       3176 
     Average |  .0664071    .047413  -.0265223   .1593366     124023      35792 
   Placebo_1 |   -.00936   .0316845  -.0714616   .0527415      73510      20655 
   Placebo_2 | -.1346116   .1695074  -.4668461   .1976228      17679       8785 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. restore

. 
. 
. 
. *Part iii): Tribal Trust
. 
. preserve

. keep if Tribal ==1
(489,733 observations deleted)

. *Baseline with no rezxt controls
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) robust_dynamic longdiff_pl
> acebo covariances average_effect graphoptions (ytitle(Agriculture (%)) graphre
> gion(color(white))  ysize(15) xtitle(Time to Treatment) xsize(20) yline(0, lpa
> ttern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0056484    .070941  -.1446928   .1333959     653896      90911 
    Effect_1 | -.1100345   .1606228  -.4248551   .2047861     439395      60457 
    Effect_2 | -.6259701   .4507608  -1.509461   .2575211     239466      22378 
     Average | -.1218665   .1446909  -.4054607   .1617277    1332757     173746 
   Placebo_1 |  .3469521   .4041899  -.4452602   1.139164     469668      90730 
   Placebo_2 |  .0620268   .0554725  -.0466994    .170753     131396      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *off-rez population
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop) robust
> _dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agri
> culture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) xs
> ize(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0036382   .0629876  -.1270938   .1198174     653890      90911 
    Effect_1 | -.1046994   .1432429  -.3854554   .1760567     439386      60457 
    Effect_2 | -.6090065   .4463931  -1.483937    .265924     239460      22378 
     Average | -.1167734   .1328543  -.3771678    .143621    1332736     173746 
   Placebo_1 |  .3450795   .3820626  -.4037632   1.093922     469659      90730 
   Placebo_2 |  .0612202   .0586379    -.05371   .1761505     131393      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *casinos
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_casino) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agr
> iculture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) )  

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0404853   .0526164  -.0626428   .1436133     653896      90911 
    Effect_1 | -.0651358   .1268475  -.3137568   .1834853     439395      60457 
    Effect_2 | -.5253941   .3920618  -1.293835    .243047     239466      22378 
     Average | -.0691505   .1091796  -.2831426   .1448416    1332757     173746 
   Placebo_1 |  .3430934   .3986105  -.4381831    1.12437     469668      90730 
   Placebo_2 |  .0589007   .0564178  -.0516782   .1694797     131396      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *credit
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(has_credit) robus
> t_dynamic longdiff_placebo covariances average_effect graphoptions (ytitle(Agr
> iculture (%)) graphregion(color(white))  ysize(15) xtitle(Time to Treatment) x
> size(20) yline(0, lpattern(dash) lcolor(gs10)) legend(off) ) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 | -.0081262   .0859019  -.1764938   .1602415     653896      90911 
    Effect_1 | -.1078034   .1515853  -.4049106   .1893039     439395      60457 
    Effect_2 | -.6164714   .4195291  -1.438748   .2058057     239466      22378 
     Average | -.1211632   .1428865  -.4012207   .1588943    1332757     173746 
   Placebo_1 |  .3469113   .4042668  -.4454516   1.139274     469668      90730 
   Placebo_2 |  .0637288   .0573677  -.0487119   .1761695     131396      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. *all rez-t controls
. did_multiplegt devpct ID         year post, placebo(2) dynamic(2) trends_nonpa
> ram(StateCode) cluster(TOWNSHIP) breps(10) seed(10) controls(offrespop has_cas
> ino has_credit) robust_dynamic longdiff_placebo covariances average_effect gra
> phoptions (ytitle(Agriculture (%)) scheme(white_hue) ysize(20) xsize(20) xline
> (-.5) legend(off)) 

DID estimators of the instantaneous treatment effect, of dynamic treatment
effects if the dynamic option is used, and of placebo tests of the parallel
trends assumption if the placebo option is used. The estimators are robust to
heterogeneous effects, and to dynamic effects if the robust_dynamic option is
used.

             |  Estimate         SE      LB CI      UB CI          N  Switchers 
-------------+------------------------------------------------------------------
    Effect_0 |  .0367531   .0752498  -.1107365   .1842427     653890      90911 
    Effect_1 | -.0733444   .1255986  -.3195176   .1728289     439386      60457 
    Effect_2 | -.5512269   .4155492  -1.365703   .2632495     239460      22378 
     Average | -.0772868   .1225696  -.3175233   .1629497    1332736     173746 
   Placebo_1 |  .3458429   .3807611  -.4004488   1.092135     469659      90730 
   Placebo_2 |  .0601297   .0610206  -.0594707   .1797301     131393      38079 

. estadd scalar tstat = e(effect_average)/e(se_effect_average)

. 
. restore

. 
. 
. 
. *Table A8, Panel B: Differential Development Impacts Across Land Tenure Regime
> s, csdid
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. *Part i): Fee Simple
. 
. preserve

. keep if  Fee ==1
(1,236,658 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. csdid devpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : regression adjustment
Treatment model: none
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0879503   .0692567    -1.27   0.204     -.223691    .0477904
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid devpct offrespop, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0868866   .0703992    -1.23   0.217    -.2248664    .0510933
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid devpct has_casino, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simp
> le) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0199719   .0460466    -0.43   0.664    -.1102216    .0702778
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit 
. csdid devpct has_credit, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simp
> le) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   -.083752   .0596618    -1.40   0.160     -.200687    .0331829
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid devpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  clust
> er(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 175,400
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 937 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0530803   .0564692    -0.94   0.347    -.1637579    .0575974
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. restore

. 
. 
. *Part ii): Allotted Trust
. 
. preserve

. keep if Allotted ==1
(1,264,533 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. csdid devpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : regression adjustment
Treatment model: none
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.1004618   .1366193    -0.74   0.462    -.3682306    .1673071
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid devpct offrespop, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .0018787   .0742855     0.03   0.980    -.1437182    .1474756
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid devpct has_casino, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simp
> le) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0544096   .1128982    -0.48   0.630     -.275686    .1668669
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid devpct has_credit, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simp
> le) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   -.290394   .2863433    -1.01   0.311    -.8516165    .2708285
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid devpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  clust
> er(TOWNSHIP) agg(simple) drimp
............
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 147,745
Outcome model  : least squares
Treatment model: inverse probability
                             (Std. err. adjusted for 748 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .1103942   .1056449     1.04   0.296     -.096666    .3174544
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. restore

. 
. 
. 
. *Part iii): Tribal Trust
. 
. preserve

. keep if  Tribal ==1
(489,733 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. csdid devpct , ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simple) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 921,140
Outcome model  : regression adjustment
Treatment model: none
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .1963611   .2259429     0.87   0.385    -.2464789    .6392011
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *off-rez population
. csdid devpct offrespop, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simpl
> e)  drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
..............xx
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 860,229
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.0487646   .6482902    -0.08   0.940     -1.31939    1.221861
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *casinos
. csdid devpct has_casino, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simp
> le) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 921,140
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |    .193281   .2262414     0.85   0.393     -.250144     .636706
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *credit
. csdid devpct has_credit, ivar(ID) time(t) gvar(TG)  cluster(TOWNSHIP) agg(simp
> le) drimp
................
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 921,140
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |   .0615016   .2464322     0.25   0.803    -.4214967       .5445
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. *all rez-t controls
. csdid devpct offrespop has_casino has_credit, ivar(ID) time(t) gvar(TG)  clust
> er(TOWNSHIP) agg(simple) drimp
Panel is not balanced
Will use observations with Pair balanced (observed at t0 and t1)
............xxxx
Difference-in-difference with Multiple Time Periods

                                                       Number of obs = 768,867
Outcome model  : least squares
Treatment model: inverse probability
                           (Std. err. adjusted for 2,517 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         ATT |  -.2092959   .3365012    -0.62   0.534    -.8688261    .4502343
------------------------------------------------------------------------------
Control: Never Treated

See Callaway and Sant'Anna (2021) for details

. 
. 
. 
. restore

. 
. *Table A8, Panel C: Differential Development Impacts Across Land Tenure Regime
> s, twfe
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. preserve 

. keep if tenure <=3
(166,108 observations deleted)

. 
. eststo clear

. *Baseline with no rezxt controls
. reghdfe devpct post allotted_post tribal_post , absorb(ID stateXyear) cluster(
> TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,244,285
Absorbing 2 HDFE groups                           F(   3,   2582) =       0.52
Statistics robust to heteroskedasticity           Prob > F        =     0.6660
                                                  R-squared       =     0.9118
                                                  Adj R-squared   =     0.8898
                                                  Within R-sq.    =     0.0001
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.2569

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
       devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |  -.0255462   .1294714    -0.20   0.844    -.2794245    .2283322
allotted_post |  -.1339809   .1116671    -1.20   0.230     -.352947    .0849851
  tribal_post |  -.0537629    .089446    -0.60   0.548    -.2291561    .1216302
        _cons |   1.403734   .0375705    37.36   0.000     1.330063    1.477405
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,244,285    1.387144    9.810134          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1595271   .2036202    -0.78   0.433    -.5588025    .2397483
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0793091   .1993333    -0.40   0.691    -.4701783    .3115602
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_tenure_1

. 
. *off-rez population
. reghdfe devpct post  allotted_post tribal_post offrespop , absorb(ID stateXyea
> r) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,244,282
Absorbing 2 HDFE groups                           F(   4,   2582) =       1.25
Statistics robust to heteroskedasticity           Prob > F        =     0.2884
                                                  R-squared       =     0.9118
                                                  Adj R-squared   =     0.8898
                                                  Within R-sq.    =     0.0002
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.2566

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
       devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |  -.0616806   .1316618    -0.47   0.639    -.3198539    .1964928
allotted_post |  -.1321028   .1110665    -1.19   0.234    -.3498912    .0856857
  tribal_post |  -.0879753   .0922428    -0.95   0.340    -.2688527     .092902
    offrespop |   1.27e-07   1.01e-07     1.26   0.208    -7.09e-08    3.25e-07
        _cons |   1.362089   .0515717    26.41   0.000     1.260963    1.463215
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,244,282    1.387019    9.809664          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1937833   .1987392    -0.98   0.330    -.5834877     .195921
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1496559   .2047825    -0.73   0.465    -.5512105    .2518987
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_tenure_2

. 
. *casinos
. reghdfe devpct post  allotted_post tribal_post has_casino, absorb(ID stateXyea
> r) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,244,285
Absorbing 2 HDFE groups                           F(   4,   2582) =       0.93
Statistics robust to heteroskedasticity           Prob > F        =     0.4448
                                                  R-squared       =     0.9118
                                                  Adj R-squared   =     0.8898
                                                  Within R-sq.    =     0.0001
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.2569

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
       devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |  -.0239234   .1119409    -0.21   0.831    -.2434264    .1955797
allotted_post |  -.1330344   .1035663    -1.28   0.199    -.3361159     .070047
  tribal_post |  -.0499321   .0579384    -0.86   0.389    -.1635425    .0636783
   has_casino |  -.0145446   .1956366    -0.07   0.941    -.3981651    .3690758
        _cons |   1.405796   .0638003    22.03   0.000     1.280691    1.530901
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,244,285    1.387144    9.810134          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1569578   .1732507    -0.91   0.365    -.4966821    .1827665
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0738555   .1374845    -0.54   0.591    -.3434465    .1957355
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_tenure_3

. 
. *credit
. reghdfe devpct post  allotted_post tribal_post has_credit, absorb(ID stateXyea
> r) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,244,285
Absorbing 2 HDFE groups                           F(   4,   2582) =       1.80
Statistics robust to heteroskedasticity           Prob > F        =     0.1257
                                                  R-squared       =     0.9119
                                                  Adj R-squared   =     0.8898
                                                  Within R-sq.    =     0.0004
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.2563

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
       devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |   .0523855   .1088428     0.48   0.630    -.1610425    .2658136
allotted_post |  -.2024867   .1285693    -1.57   0.115     -.454596    .0496226
  tribal_post |  -.1292259   .1183176    -1.09   0.275    -.3612329     .102781
   has_credit |   .2978943   .1496077     1.99   0.047     .0045311    .5912575
        _cons |   1.245374   .0595071    20.93   0.000     1.128687     1.36206
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,244,285    1.387144    9.810134          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1501012   .1988919    -0.75   0.451     -.540105    .2399026
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0768404   .1971866    -0.39   0.697    -.4635002    .3098194
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_tenure_4

. 
. *all rez-t controls
. reghdfe devpct post  allotted_post tribal_post offrespop has_casino has_credit
> , absorb(ID stateXyear) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,244,282
Absorbing 2 HDFE groups                           F(   6,   2582) =       2.04
Statistics robust to heteroskedasticity           Prob > F        =     0.0570
                                                  R-squared       =     0.9119
                                                  Adj R-squared   =     0.8898
                                                  Within R-sq.    =     0.0005
Number of clusters (TOWNSHIP) =      2,583        Root MSE        =     3.2561

                            (Std. err. adjusted for 2,583 clusters in TOWNSHIP)
-------------------------------------------------------------------------------
              |               Robust
       devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------+----------------------------------------------------------------
         post |   .0291795   .0982257     0.30   0.766    -.1634296    .2217886
allotted_post |  -.1902122   .1231916    -1.54   0.123    -.4317765    .0513521
  tribal_post |  -.1262301    .084328    -1.50   0.135    -.2915874    .0391272
    offrespop |   1.07e-07   1.12e-07     0.95   0.343    -1.14e-07    3.27e-07
   has_casino |  -.1038712   .2290783    -0.45   0.650    -.5530669    .3453245
   has_credit |   .2807641   .1650304     1.70   0.089    -.0428412    .6043693
        _cons |    1.23425   .0618348    19.96   0.000     1.112999      1.3555
-------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    248857      248857           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,244,282    1.387019    9.809664          0        100

. estadd scalar MDV = r(mean)

. lincom  post + allotted_post

 ( 1)  post + allotted_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.1610327   .1670941    -0.96   0.335    -.4886846    .1666193
------------------------------------------------------------------------------

. estadd scalar adiff = r(estimate)

. estadd scalar ase_diff = r(se)

. estadd scalar apval = 2*normal(-abs(r(estimate)/r(se)))

. lincom  post + tribal_post

 ( 1)  post + tribal_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0970506   .1458968    -0.67   0.506    -.3831372    .1890361
------------------------------------------------------------------------------

. estadd scalar tdiff = r(estimate)

. estadd scalar tse_diff = r(se)

. estadd scalar tpval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_tenure_5

. 
. 
. esttab twfe_dev_tenure*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  repla
> ce   scalar(N_clust M1 MDV adiff apval tdiff tpval)

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                   devpct          devpct          devpct          devpct       
>    devpct   
--------------------------------------------------------------------------------
> ------------
post              -0.0255         -0.0617         -0.0239          0.0524       
>    0.0292   
                  (0.129)         (0.132)         (0.112)         (0.109)       
>  (0.0982)   

allotted_p~t       -0.134          -0.132          -0.133          -0.202       
>    -0.190   
                  (0.112)         (0.111)         (0.104)         (0.129)       
>   (0.123)   

tribal_post       -0.0538         -0.0880         -0.0499          -0.129       
>    -0.126   
                 (0.0894)        (0.0922)        (0.0579)         (0.118)       
>  (0.0843)   

offrespop                     0.000000127                                     0.
> 000000107   
                             (0.000000101)                                    (0
> .000000112)   

has_casino                                        -0.0145                       
>    -0.104   
                                                  (0.196)                       
>   (0.229)   

has_credit                                                          0.298**     
>     0.281*  
                                                                  (0.150)       
>   (0.165)   

_cons               1.404***        1.362***        1.406***        1.245***    
>     1.234***
                 (0.0376)        (0.0516)        (0.0638)        (0.0595)       
>  (0.0618)   
--------------------------------------------------------------------------------
> ------------
N                 1244285         1244282         1244285         1244285       
>   1244282   
adj. R-sq           0.890           0.890           0.890           0.890       
>     0.890   
N_clust              2583            2583            2583            2583       
>      2583   
M1                                                                              
>             
MDV                 1.387           1.387           1.387           1.387       
>     1.387   
adiff              -0.160          -0.194          -0.157          -0.150       
>    -0.161   
apval               0.433           0.330           0.365           0.450       
>     0.335   
tdiff             -0.0793          -0.150         -0.0739         -0.0768       
>   -0.0971   
tpval               0.691           0.465           0.591           0.697       
>     0.506   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. restore

. 
end of do-file

. 
. *Table A9: Differential Impacts by Leasing Status
. do "run_table_A9.do"

. *This file runs the regressions for Table A9
. 
. 
. 
. *Table A9, Panel A: Differential Impacts by Leasing Stats, Agriculture
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. eststo clear

. *Baseline with no rezxt controls
. reghdfe agpct post lease_post , absorb(ID stateXyear) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   2,   2630) =       8.57
Statistics robust to heteroskedasticity           Prob > F        =     0.0002
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5419

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2763182   .0674864     4.09   0.000     .1439863    .4086501
  lease_post |  -.2717557   .1564682    -1.74   0.083    -.5785689    .0350575
       _cons |    8.10063   .0131911   614.10   0.000     8.074764    8.126496
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0045625   .1520349     0.03   0.976    -.2935576    .3026826
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_lease_1

. 
. *off-rez population
. reghdfe agpct post  lease_post offrespop , absorb(ID stateXyear) cluster(TOWNS
> HIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   3,   2630) =       8.44
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0005
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5415

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3643491   .0755939     4.82   0.000     .2161197    .5125786
  lease_post |  -.2853689   .1561616    -1.83   0.068    -.5915809     .020843
   offrespop |  -1.87e-07   5.89e-08    -3.18   0.001    -3.03e-07   -7.19e-08
       _cons |   8.161817   .0244332   334.05   0.000     8.113907    8.209728
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0789802   .1390342     0.57   0.570    -.1936472    .3516076
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_lease_2

. 
. *casinos
. reghdfe agpct post  lease_post has_casino, absorb(ID stateXyear) cluster(TOWNS
> HIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =      39.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5405

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .3924929    .065468     6.00   0.000     .2641189    .5208668
  lease_post |  -.1965246   .1521387    -1.29   0.197    -.4948482    .1017991
  has_casino |  -.4609233   .0517569    -8.91   0.000    -.5624117   -.3594348
       _cons |   8.168429   .0167825   486.72   0.000      8.13552    8.201337
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1959683   .1471132     1.33   0.183     -.092501    .4844376
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_lease_3

. 
. *credit
. reghdfe agpct post lease_post has_credit, absorb(ID stateXyear) cluster(TOWNSH
> IP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =       5.81
Statistics robust to heteroskedasticity           Prob > F        =     0.0006
                                                  R-squared       =     0.9833
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5419

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .2634094   .0639656     4.12   0.000     .1379815    .3888373
  lease_post |  -.2447147   .1524175    -1.61   0.108     -.543585    .0541556
  has_credit |  -.0890142   .0761616    -1.17   0.243    -.2383569    .0603284
       _cons |   8.148353   .0388474   209.75   0.000     8.072178    8.224527
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,185    8.146545    24.54447          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0186947   .1512031     0.12   0.902    -.2777943    .3151837
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_lease_4

. 
. *all rez-t controls
. reghdfe agpct post lease_post offrespop has_casino has_credit, absorb(ID state
> Xyear) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   5,   2630) =      26.02
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.9834
                                                  Adj R-squared   =     0.9792
                                                  Within R-sq.    =     0.0011
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.5405

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |   .4130958    .069283     5.96   0.000     .2772411    .5489504
  lease_post |  -.2028775   .1556992    -1.30   0.193    -.5081827    .1024278
   offrespop |  -6.60e-08   6.23e-08    -1.06   0.290    -1.88e-07    5.62e-08
  has_casino |  -.4272306   .0544677    -7.84   0.000    -.5340345   -.3204267
  has_credit |  -.0129543      .0784    -0.17   0.869    -.1666863    .1407777
       _cons |   8.191958   .0401426   204.07   0.000     8.113244    8.270672
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum agpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
       agpct |  1,410,182    8.146562    24.54449          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
       agpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2102183   .1413331     1.49   0.137     -.066917    .4873536
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_ag_lease_5

. 
. 
. esttab twfe_ag_lease*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  replace
>    scalar(N_clust M1 MDV diff pval)

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                    agpct           agpct           agpct           agpct       
>     agpct   
--------------------------------------------------------------------------------
> ------------
post                0.276***        0.364***        0.392***        0.263***    
>     0.413***
                 (0.0675)        (0.0756)        (0.0655)        (0.0640)       
>  (0.0693)   

lease_post         -0.272*         -0.285*         -0.197          -0.245       
>    -0.203   
                  (0.156)         (0.156)         (0.152)         (0.152)       
>   (0.156)   

offrespop                    -0.000000187***                                    
> -6.60e-08   
                               (5.89e-08)                                      (
> 6.23e-08)   

has_casino                                         -0.461***                    
>    -0.427***
                                                 (0.0518)                       
>  (0.0545)   

has_credit                                                        -0.0890       
>   -0.0130   
                                                                 (0.0762)       
>  (0.0784)   

_cons               8.101***        8.162***        8.168***        8.148***    
>     8.192***
                 (0.0132)        (0.0244)        (0.0168)        (0.0388)       
>  (0.0401)   
--------------------------------------------------------------------------------
> ------------
N                 1410185         1410182         1410185         1410185       
>   1410182   
adj. R-sq           0.979           0.979           0.979           0.979       
>     0.979   
N_clust              2631            2631            2631            2631       
>      2631   
M1                                                                              
>             
MDV                 8.147           8.147           8.147           8.147       
>     8.147   
diff              0.00456          0.0790           0.196          0.0187       
>     0.210   
pval                0.976           0.570           0.183           0.902       
>     0.137   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. *Table A9, Panel A: Differential Impacts by Leasing Stats, Development
. ******************************************************************************
> *
. ******************************************************************************
> *
. 
. 
. 
. eststo clear

. *Baseline with no rezxt controls
. reghdfe devpct post lease_post , absorb(ID stateXyear) cluster(TOWNSHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   2,   2630) =       2.70
Statistics robust to heteroskedasticity           Prob > F        =     0.0676
                                                  R-squared       =     0.9104
                                                  Adj R-squared   =     0.8879
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.2106

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
      devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.1576345   .1409236    -1.12   0.263    -.4339667    .1186978
  lease_post |   .4282532   .2394712     1.79   0.074    -.0413178    .8978243
       _cons |   1.364167   .0312228    43.69   0.000     1.302943    1.425391
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,410,185    1.347834    9.591242          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2706188   .2998648     0.90   0.367    -.3173761    .8586136
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_lease_1

. 
. *off-rez population
. reghdfe devpct post  lease_post offrespop , absorb(ID stateXyear) cluster(TOWN
> SHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   3,   2630) =       2.33
Statistics robust to heteroskedasticity           Prob > F        =     0.0724
                                                  R-squared       =     0.9104
                                                  Adj R-squared   =     0.8880
                                                  Within R-sq.    =     0.0006
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.2102

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
      devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.2385416   .1437657    -1.66   0.097    -.5204469    .0433638
  lease_post |    .440707   .2388684     1.84   0.065     -.027682    .9090959
   offrespop |   1.72e-07   1.04e-07     1.65   0.098    -3.20e-08    3.77e-07
       _cons |   1.307823   .0504003    25.95   0.000     1.208995    1.406651
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,410,182    1.347724    9.590816          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2021654   .2870252     0.70   0.481    -.3606528    .7649836
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_lease_2

. 
. *casinos
. reghdfe devpct post  lease_post has_casino, absorb(ID stateXyear) cluster(TOWN
> SHIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =       2.58
Statistics robust to heteroskedasticity           Prob > F        =     0.0521
                                                  R-squared       =     0.9104
                                                  Adj R-squared   =     0.8879
                                                  Within R-sq.    =     0.0003
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.2106

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
      devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.1600455   .1041302    -1.54   0.124     -.364231    .0441399
  lease_post |   .4266919   .2319975     1.84   0.066    -.0282242     .881608
  has_casino |    .009566   .1793836     0.05   0.957    -.3421814    .3613133
       _cons |    1.36276   .0558168    24.41   0.000      1.25331    1.472209
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,410,185    1.347834    9.591242          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2666463   .2572278     1.04   0.300    -.2377431    .7710358
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_lease_3

. 
. *credit
. reghdfe devpct post lease_post has_credit, absorb(ID stateXyear) cluster(TOWNS
> HIP)
(MWFE estimator converged in 2 iterations)

HDFE Linear regression                            Number of obs   =  1,410,185
Absorbing 2 HDFE groups                           F(   3,   2630) =       2.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0670
                                                  R-squared       =     0.9104
                                                  Adj R-squared   =     0.8880
                                                  Within R-sq.    =     0.0006
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.2102

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
      devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.1208585   .1265704    -0.95   0.340    -.3690462    .1273292
  lease_post |    .351216   .2446532     1.44   0.151    -.1285162    .8309483
  has_credit |   .2535927   .1335973     1.90   0.058    -.0083738    .5155591
       _cons |   1.228209   .0552555    22.23   0.000      1.11986    1.336557
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,410,185    1.347834    9.591242          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2303576    .307563     0.75   0.454    -.3727324    .8334475
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_lease_4

. 
. *all rez-t controls
. reghdfe devpct post lease_post offrespop has_casino has_credit, absorb(ID stat
> eXyear) cluster(TOWNSHIP)
(MWFE estimator converged in 3 iterations)

HDFE Linear regression                            Number of obs   =  1,410,182
Absorbing 2 HDFE groups                           F(   5,   2630) =       2.12
Statistics robust to heteroskedasticity           Prob > F        =     0.0600
                                                  R-squared       =     0.9104
                                                  Adj R-squared   =     0.8880
                                                  Within R-sq.    =     0.0008
Number of clusters (TOWNSHIP) =      2,631        Root MSE        =     3.2099

                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
      devpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
        post |  -.1779924   .1005467    -1.77   0.077     -.375151    .0191663
  lease_post |   .3903149   .2397489     1.63   0.104    -.0798006    .8604304
   offrespop |   1.60e-07   1.14e-07     1.41   0.160    -6.35e-08    3.84e-07
  has_casino |  -.0949683    .205097    -0.46   0.643    -.4971361    .3071996
  has_credit |   .2140735   .1446359     1.48   0.139    -.0695382    .4976851
       _cons |   1.210891    .057652    21.00   0.000     1.097843    1.323939
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
          ID |    282037      282037           0    *|
  stateXyear |        45           0          45     |
-----------------------------------------------------+
* = FE nested within cluster; treated as redundant for DoF computation

. sum devpct if e(sample) ==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      devpct |  1,410,182    1.347724    9.590816          0        100

. estadd scalar MDV = r(mean)

. lincom  post + lease_post

 ( 1)  post + lease_post = 0

------------------------------------------------------------------------------
      devpct | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .2123225   .2585172     0.82   0.412    -.2945952    .7192403
------------------------------------------------------------------------------

. estadd scalar diff = r(estimate)

. estadd scalar se_diff = r(se)

. estadd scalar pval = 2*normal(-abs(r(estimate)/r(se)))

. est sto twfe_dev_lease_5

. 
. 
. esttab twfe_dev_lease*,  se(a3) b(a3) star(* 0.1 ** 0.05 *** 0.01) ar2  replac
> e   scalar(N_clust M1 MDV diff pval)

--------------------------------------------------------------------------------
> ------------
                      (1)             (2)             (3)             (4)       
>       (5)   
                   devpct          devpct          devpct          devpct       
>    devpct   
--------------------------------------------------------------------------------
> ------------
post               -0.158          -0.239*         -0.160          -0.121       
>    -0.178*  
                  (0.141)         (0.144)         (0.104)         (0.127)       
>   (0.101)   

lease_post          0.428*          0.441*          0.427*          0.351       
>     0.390   
                  (0.239)         (0.239)         (0.232)         (0.245)       
>   (0.240)   

offrespop                     0.000000172*                                    0.
> 000000160   
                             (0.000000104)                                    (0
> .000000114)   

has_casino                                        0.00957                       
>   -0.0950   
                                                  (0.179)                       
>   (0.205)   

has_credit                                                          0.254*      
>     0.214   
                                                                  (0.134)       
>   (0.145)   

_cons               1.364***        1.308***        1.363***        1.228***    
>     1.211***
                 (0.0312)        (0.0504)        (0.0558)        (0.0553)       
>  (0.0577)   
--------------------------------------------------------------------------------
> ------------
N                 1410185         1410182         1410185         1410185       
>   1410182   
adj. R-sq           0.888           0.888           0.888           0.888       
>     0.888   
N_clust              2631            2631            2631            2631       
>      2631   
M1                                                                              
>             
MDV                 1.348           1.348           1.348           1.348       
>     1.348   
diff                0.271           0.202           0.267           0.230       
>     0.212   
pval                0.367           0.481           0.300           0.454       
>     0.411   
--------------------------------------------------------------------------------
> ------------
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
end of do-file

. 
. 
. *Figure A5: Adjudication Start Dates and Other Covariates
. do "run_figure_A5.do"

. *This file produces the graphs for Figure A5
. 
. preserve

. 
. 
. gen offrezpop74 = offrespop
(21 missing values generated)

. replace offrezpop74 = . if year !=1974
(1,129,909 real changes made, 1,129,909 to missing)

. 
. gen agpct74 = agpct
(2,208 missing values generated)

. replace agpct74 = . if year !=1974
(1,128,160 real changes made, 1,128,160 to missing)

. 
. gen devpct74 = devpct
(2,208 missing values generated)

. replace devpct74 = . if year !=1974
(1,128,160 real changes made, 1,128,160 to missing)

. 
. gen n = 1

. 
. collapse (mean) STARTDUM  tMean pptMean  resolution SoilStd ElevMean Ruggednes
> s SoilMean StreamDist casino_year credit_yr agpct74 devpct74 offrezpop74 adj_s
> tart (max) post (sum) n, by(rezname)

. 
. ren post Treated

. gen lnpop = ln(offrez)
(3 missing values generated)

. 
. 
. *Panel A: 1974 Agriculture
. twoway lfitci  agpct adj if Treated ==0, lcolor(maroon) lpattern(dash) alwidth
> (none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci agpct adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs13) || 
> ///
> scatter agpct adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) || ///
> scatter agpct adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("Mean % Agriculture in 1974")

. 
. 
. *Panel B: 1974 Development
. twoway lfitci  devp adj if Treated ==0, lcolor(maroon) lpattern(dash) alwidth(
> none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci devp adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs13) || /
> //
> scatter devp adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) || ///
> scatter devp adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("Mean % Developed in 1974")

. 
. 
. *Panel C: 1974 Population
. twoway lfitci  lnpop adj if Treated ==0, lcolor(maroon) lpattern(dash) alwidth
> (none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci lnpop adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs13) || 
> ///
> scatter lnpop adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) || ///
> scatter lnpop adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("ln(Off-Reservation Population in 1974)")

. 
. 
. 
. *Panel D: Date of First Casino
. twoway lfitci  casino_year adj if Treated ==0, lcolor(maroon) lpattern(dash) a
> lwidth(none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci casino_year adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs1
> 3) || ///
> scatter casino_year adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) |
> | ///
> scatter casino_year adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("Date of First Casino")

. 
. 
. *Panel E: Date of First Banking Access
. twoway lfitci  credit_yr adj if Treated ==0, lcolor(maroon) lpattern(dash) alw
> idth(none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci credit_yr adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs13)
>  || ///
> scatter credit_yr adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) || 
> ///
> scatter credit_yr adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("Date of First Banking Access")

. 
. 
. *Panel F: Stream Distance
. twoway lfitci  StreamDist adj if Treated ==0, lcolor(maroon) lpattern(dash) al
> width(none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci StreamDist adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs13
> ) || ///
> scatter StreamDist adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) ||
>  ///
> scatter StreamDist adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("Mean Distance to Nearest Stream (meters)")

. 
.  
. 
. *Panel G: Precipitation 
. twoway lfitci  pptMean adj if Treated ==0, lcolor(maroon) lpattern(dash) alwid
> th(none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci pptMean adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs13) |
> | ///
> scatter pptMean adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) || //
> /
> scatter pptMean adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("Mean Precipitation (mm)")

. 
. 
. 
. *Panel H: Temperature
. twoway lfitci  tMean adj if Treated ==0, lcolor(maroon) lpattern(dash) alwidth
> (none) fcolor(gs13)       fintensity(inten50) || ///
> lfitci tMean adj if Treated ==1, lcolor(maroon) alwidth(none) fcolor(gs13) || 
> ///
> scatter tMean adj if Treated == 0, mcolor(navy) msymbol(circle_hollow) || ///
> scatter tMean adj if Treated == 1, mcolor(navy) msymbol(circle)  ///
> graphr(color(white)) legend( order( 5  "Untreated" 6 "Treated" 2 "Linear Fit, 
> Unreated" 4 "Linear Fit, Treated" ) cols(2))  ///
> xtitle("Adjudication Start Date") ///
> ytitle("Mean Temperature (deg C)")

. 
. 
. restore

. 
. 
. 
end of do-file

. *Figure A7: Agricultural Land Use Event Study — Sun and Abraham (2021) Estimat
> or
. do "run_figure_A7.do"

. //This file runs the regressions to produce the plots in figure A7.
. ******************************************************************************
> *
. 
. 
. //Build Variables for Sun and Abraham
. 
. gen decade = .
variable decade already defined
r(110);

end of do-file
r(110);

end of do-file

r(110);

. 
. 
. 
. *Panel A: No Controls

. 
. eventstudyinteract agpct  RT1 RT2 RT3  RT5 RT6 RT7 if regsamp ==1 , absorb(ID 
> stateXyear) cohort(COHORT) control_cohort(CONTROL) vce( cl TOWNSHIP) 
(obs=759,080)

IW estimates for dynamic effects                     Number of obs = 1,410,182
Absorbing 2 HDFE groups                              F(15, 2630)   =     10.91
                                                     Prob > F      =    0.0000
                                                     R-squared     =    0.9834
                                                     Adj R-squared =    0.9792
                                                     Root MSE      =    3.5402
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         RT1 |  -.3754069   .1562765    -2.40   0.016    -.6818443   -.0689695
         RT2 |   .0510329   .0745364     0.68   0.494    -.0951231    .1971889
         RT3 |   .0413055   .0521198     0.79   0.428    -.0608945    .1435056
         RT5 |   .1932793   .0503295     3.84   0.000     .0945899    .2919687
         RT6 |   .4726167   .0872307     5.42   0.000      .301569    .6436645
         RT7 |   .6718119   .2584776     2.60   0.009     .1649717    1.178652
------------------------------------------------------------------------------

. 
. 
. 
.         matrix C = e(b_iw)

. 
.         mata st_matrix("A",sqrt(diagonal(st_matrix("e(V_iw)"))))

. 
.         matrix C = C \ A'

. 
.         matrix list C

C[2,6]
           RT1         RT2         RT3         RT5         RT6         RT7
r1  -.37540692   .05103292   .04130554   .19327932   .47261672   .67181185
c1   .15627653   .07453643   .05211982   .05032947   .08723069   .25847763

. 
. matrix B = C[1...,2..3]

. 
. matrix D = [0\0]

. 
. matrix E = C[1...,4..6]

. 
. matrix F = B,D,E

. 
.         coefplot matrix(F[1]), se(F[2]) vertical yline(0, lpattern(dash) lcolo
> r(gs10)) recast(connected) graphregion(color(white)) xlabel(1 "-3" 2 "-2" 3 "-
> 1" 4 "0" 5 "1" 6 "2") xtitle("Time to Treatment") ytitle("Agriculture (%)")

. 
. 
. 
. *Panel B: Off-Reservation Population Control

. 
. eventstudyinteract agpct  RT1 RT2 RT3  RT5 RT6 RT7 if regsamp ==1 , absorb(ID 
> stateXyear) cohort(COHORT) control_cohort(CONTROL) vce( cl TOWNSHIP) covariate
> s(offrespop)
(obs=759,080)

IW estimates for dynamic effects                     Number of obs = 1,410,182
Absorbing 2 HDFE groups                              F(16, 2630)   =     11.11
                                                     Prob > F      =    0.0000
                                                     R-squared     =    0.9834
                                                     Adj R-squared =    0.9792
                                                     Root MSE      =    3.5379
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         RT1 |  -.6853335   .1717263    -3.99   0.000    -1.022066   -.3486011
         RT2 |  -.0553613   .0786526    -0.70   0.482    -.2095885    .0988659
         RT3 |  -.0723412   .0573848    -1.26   0.208    -.1848652    .0401828
         RT5 |    .321628   .0517282     6.22   0.000     .2201959    .4230601
         RT6 |   .7932856   .1087734     7.29   0.000     .5799954    1.006576
         RT7 |   2.030326   .4028453     5.04   0.000     1.240401    2.820252
------------------------------------------------------------------------------

. 
. 
. 
.         matrix C = e(b_iw)

. 
.         mata st_matrix("A",sqrt(diagonal(st_matrix("e(V_iw)"))))

. 
.         matrix C = C \ A'

. 
.         matrix list C

C[2,6]
           RT1         RT2         RT3         RT5         RT6         RT7
r1  -.68533345  -.05536128   -.0723412   .32162798   .79328563   2.0303264
c1   .17172631   .07865258   .05738482   .05172819   .10877344   .40284527

. 
.         matrix B = C[1...,2..3]

. 
. matrix D = [0\0]

. 
. matrix E = C[1...,4..6]

. 
. matrix F = B,D,E

. 
.         coefplot matrix(F[1]), se(F[2]) vertical yline(0, lpattern(dash) lcolo
> r(gs10)) recast(connected) graphregion(color(white)) xlabel(1 "-3" 2 "-2" 3 "-
> 1" 4 "0" 5 "1" 6 "2") xtitle("Time to Treatment") ytitle("Agriculture (%)")

. 
. 
. 
. 
. 
. *Panel C: Casino Control

. 
. eventstudyinteract agpct  RT1 RT2 RT3  RT5 RT6 RT7 if regsamp ==1 , absorb(ID 
> stateXyear) cohort(COHORT) control_cohort(CONTROL) vce( cl TOWNSHIP) covariate
> s(has_casino)
(obs=759,080)

IW estimates for dynamic effects                     Number of obs = 1,410,182
Absorbing 2 HDFE groups                              F(16, 2630)   =     15.52
                                                     Prob > F      =    0.0000
                                                     R-squared     =    0.9834
                                                     Adj R-squared =    0.9792
                                                     Root MSE      =    3.5382
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         RT1 |   -.548277   .1564553    -3.50   0.000    -.8550649   -.2414891
         RT2 |  -.1250147   .0734585    -1.70   0.089    -.2690571    .0190277
         RT3 |  -.1221386   .0522099    -2.34   0.019    -.2245152   -.0197619
         RT5 |   .2576884   .0505958     5.09   0.000     .1584768    .3568999
         RT6 |   .5927906   .0857962     6.91   0.000     .4245556    .7610256
         RT7 |   1.128908   .2614576     4.32   0.000      .616225    1.641592
------------------------------------------------------------------------------

. 
. 
. 
.         matrix C = e(b_iw)

. 
.         mata st_matrix("A",sqrt(diagonal(st_matrix("e(V_iw)"))))

. 
.         matrix C = C \ A'

. 
.         matrix list C

C[2,6]
           RT1         RT2         RT3         RT5         RT6         RT7
r1  -.54827698  -.12501471  -.12213856   .25768836    .5927906   1.1289084
c1   .15645528   .07345854   .05220993   .05059579   .08579624   .26145758

. 
. matrix B = C[1...,2..3]

. 
. matrix D = [0\0]

. 
. matrix E = C[1...,4..6]

. 
. matrix F = B,D,E

. 
.         coefplot matrix(F[1]), se(F[2]) vertical yline(0, lpattern(dash) lcolo
> r(gs10)) recast(connected) graphregion(color(white)) xlabel(1 "-3" 2 "-2" 3 "-
> 1" 4 "0" 5 "1" 6 "2") xtitle("Time to Treatment") ytitle("Agriculture (%)")

. 
. 
. 
. 
. 
. *Panel D: Credit Access Control

. 
. eventstudyinteract agpct  RT1 RT2 RT3  RT5 RT6 RT7 if regsamp ==1 , absorb(ID 
> stateXyear) cohort(COHORT) control_cohort(CONTROL) vce( cl TOWNSHIP) covariate
> s(has_credit)
(obs=759,080)

IW estimates for dynamic effects                     Number of obs = 1,410,182
Absorbing 2 HDFE groups                              F(16, 2630)   =     11.68
                                                     Prob > F      =    0.0000
                                                     R-squared     =    0.9834
                                                     Adj R-squared =    0.9792
                                                     Root MSE      =    3.5399
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         RT1 |  -.3856642   .1573755    -2.45   0.014    -.6942565   -.0770719
         RT2 |   .0748976    .072484     1.03   0.302    -.0672337     .217029
         RT3 |   .0379907   .0526087     0.72   0.470    -.0651679    .1411493
         RT5 |   .1713014   .0490846     3.49   0.000     .0750531    .2675496
         RT6 |   .4967112   .0871704     5.70   0.000     .3257817    .6676407
         RT7 |   .8175788   .2580264     3.17   0.002     .3116234    1.323534
------------------------------------------------------------------------------

. 
. 
. 
.         matrix C = e(b_iw)

. 
.         mata st_matrix("A",sqrt(diagonal(st_matrix("e(V_iw)"))))

. 
.         matrix C = C \ A'

. 
.         matrix list C

C[2,6]
          RT1        RT2        RT3        RT5        RT6        RT7
r1  -.3856642  .07489764  .03799069  .17130138  .49671119  .81757882
c1  .15737548  .07248397  .05260869  .04908455   .0871704  .25802645

. 
.         matrix B = C[1...,2..3]

. 
. matrix D = [0\0]

. 
. matrix E = C[1...,4..6]

. 
. matrix F = B,D,E

. 
.         coefplot matrix(F[1]), se(F[2]) vertical yline(0, lpattern(dash) lcolo
> r(gs10)) recast(connected) graphregion(color(white)) xlabel(1 "-3" 2 "-2" 3 "-
> 1" 4 "0" 5 "1" 6 "2") xtitle("Time to Treatment") ytitle("Agriculture (%)")

. 
. 
. 
. 
. 
. *Panel E: All Controls

. 
. eventstudyinteract agpct  RT1 RT2 RT3  RT5 RT6 RT7 if regsamp ==1 , absorb(ID 
> stateXyear) cohort(COHORT) control_cohort(CONTROL) vce( cl TOWNSHIP) covariate
> s(offrespop has_casino has_credit)
(obs=759,080)

IW estimates for dynamic effects                     Number of obs = 1,410,182
Absorbing 2 HDFE groups                              F(18, 2630)   =     16.55
                                                     Prob > F      =    0.0000
                                                     R-squared     =    0.9834
                                                     Adj R-squared =    0.9792
                                                     Root MSE      =    3.5366
                           (Std. err. adjusted for 2,631 clusters in TOWNSHIP)
------------------------------------------------------------------------------
             |               Robust
       agpct | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         RT1 |  -.7621137   .1712082    -4.45   0.000     -1.09783   -.4263974
         RT2 |  -.1513583   .0735616    -2.06   0.040    -.2956028   -.0071138
         RT3 |    -.17574   .0559059    -3.14   0.002     -.285364   -.0661159
         RT5 |   .3303088   .0502013     6.58   0.000     .2318707    .4287468
         RT6 |   .8375111    .106016     7.90   0.000     .6296278    1.045394
         RT7 |   2.207887    .394684     5.59   0.000     1.433965     2.98181
------------------------------------------------------------------------------

. 
. 
. 
.         matrix C = e(b_iw)

. 
.         mata st_matrix("A",sqrt(diagonal(st_matrix("e(V_iw)"))))

. 
.         matrix C = C \ A'

. 
.         matrix list C

C[2,6]
           RT1         RT2         RT3         RT5         RT6         RT7
r1  -.76211375  -.15135831  -.17573996   .33030876   .83751106   2.2078872
c1   .17120817    .0735616   .05590594   .05020131   .10601604   .39468402

. 
.         matrix B = C[1...,2..3]

. 
. matrix D = [0\0]

. 
. matrix E = C[1...,4..6]

. 
. matrix F = B,D,E

. 
.         coefplot matrix(F[1]), se(F[2]) vertical yline(0, lpattern(dash) lcolo
> r(gs10)) recast(connected) graphregion(color(white)) xlabel(1 "-3" 2 "-2" 3 "-
> 1" 4 "0" 5 "1" 6 "2") xtitle("Time to Treatment") ytitle("Agriculture (%)")

. 
. *Figure A8: Distribution of 1974 Agricultural Land Use
. do "run_figure_A8.do"

. *This file produces figure A8
. 
. preserve 

. 
. duplicates drop rezname, force

Duplicates in terms of rezname

(1,412,350 observations deleted)

. 
. hist RezAg, graphregion(color(white) lcolor(black)) title("") fcolor(navy) lco
> lor(navy) fintensity(inten80 ) w(.05) xtitle("% Agricultural Land Use By Reser
> vation, 1974")
(bin=17, start=0, width=.05)

. 
. 
. restore

. 
end of do-file

. 
. 
. 
. use "FigA1.dta", clear //Uses data from Sanchez (2022))

. 
. *Figure A1: Relative Magnitude and Priority of Tribal Water Rights
. do "run_figure_A1.do"

. *This file produces a graph for figure A1
. 
. 
. ****Variable Definitions: 
. 
. *settlement: Tribal water settlement identification
. *settlementPct: water right settlement volume as a percentage of total settlem
> ent volume
. *priorityOrder: priority rank of each water right within a settlement. A lower
>  rank indicates a more senior right.
. *bargainingParty: Bargaining party type within settlement
. *id_code2: for graphing purposes, groups bargaining party and water right prio
> rity group within a settlement
. ******************************************************************************
> **
. 
. 
. 
. colorpalette "42 72 88" "86 191 130", ipolate(100, HCL power(2.5))

. graph bar (sum) settlementPct, over(id_code2) over(settlement, label(labsize(s
> mall) angle(45))) asyvars stack ///
> ytitle("Post-Settlement Water Right Volume (%)" "<– Senior - Junior –>") ylabe
> l(, angle(0) format(%12.0gc)) ///
> bar(1, c(navy) lcolor(none)) ///
> bar(2, c(gs10) lcolor(none)) ///
> bar(3, c(navy) lcolor(none)) ///
> bar(4, c(gs10) lcolor(none)) ///
> bar(5, c(navy) lcolor(none)) ///
> bar(6, c(gs10) lcolor(none)) ///
> bar(7, c(navy) lcolor(none)) ///
> bar(8, c(gs10) lcolor(none)) ///
> bar(9, c(navy) lcolor(none)) ///
> bar(10, c(gs10) lcolor(none)) ///
> bar(11, c(navy) lcolor(none)) ///
> bar(12, c(gs10) lcolor(none)) ///
> bar(13, c(navy) lcolor(none)) ///
> bar(14, c(gs10) lcolor(none)) ///
> bar(15, c(navy) lcolor(none)) ///
> bar(16, c(gs10) lcolor(none)) /// nez perce IDs
> bar(17, c(navy) lcolor(none)) ///
> bar(18, c(gs10) lcolor(none)) ///
> bar(19, c(navy) lcolor(none)) ///
> bar(20, c(gs10) lcolor(none)) ///
> bar(21, c(navy) lcolor(none)) ///
> bar(22, c(gs10) lcolor(none)) ///
> bar(23, c(navy) lcolor(none)) ///
> bar(24, c(gs10) lcolor(none)) ///
> bar(25, c(navy) lcolor(none)) ///
> bar(26, c(gs10) lcolor(none)) ///
> bar(27, c(navy) lcolor(none)) ///
> bar(28, c(gs10) lcolor(none)) ///
> bar(29, c(navy) lcolor(none)) ///
> bar(30, c(gs10) lcolor(none)) ///
> bar(31, c(navy) lcolor(none)) ///
> bar(32, c(gs10) lcolor(none)) ///
> bar(33, c(navy) lcolor(none)) ///
> bar(34, c(gs10) lcolor(none)) ///
> bar(35, c(navy) lcolor(none)) ///
> bar(36, c(navy) lcolor(none)) ///
> bar(37, c(gs10) lcolor(none)) ///
> bar(38, c(navy) lcolor(none)) ///
> bar(39, c(gs10) lcolor(none)) ///
> bar(40, c(navy) lcolor(none)) ///
> bar(41, c(gs10) lcolor(none)) ///
> bar(42, c(navy) lcolor(none)) ///
> bar(43, c(gs10) lcolor(none)) ///
> bar(44, c(navy) lcolor(none)) ///
> bar(45, c(gs10) lcolor(none)) ///
> bar(46, c(navy) lcolor(none)) ///
> bar(47, c(gs10) lcolor(none)) ///
> bar(48, c(navy) lcolor(none)) ///
> bar(49, c(gs10) lcolor(none)) ///
> legend(order(1 "Tribe" 2 "Irrigation Districts") pos(6) col(2))

. 
end of do-file

. 
. 
. 
. 
. log close
      name:  <unnamed>
       log:  C:\Users\ecedwar2\Dropbox\Land and Water\empirics\JAERE_Replication
> \SEL_Winters_Replication\replication_log.log
  log type:  text
 closed on:  29 Mar 2023, 14:56:52
--------------------------------------------------------------------------------
